Tree Models

Hetero SecureBoost

Gradient Boosting Decision Tree(GBDT) is a widely used statistic model for classification and regression problems. FATE provides a novel lossless privacy-preserving tree-boosting system known as SecureBoost: A Lossless Federated Learning Framework.

This federated learning system allows a learning process to be jointly conducted over multiple parties with partially common user samples but different feature sets, which corresponds to a vertically partitioned data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non privacy-preserving approach while revealing no information on private data.

The following figure shows the proposed Federated SecureBoost framework.

• Active Party

  We define the active party as the data provider who holds both a data matrix and the class label. Since the class label information is indispensable for supervised learning, there must be an active party with access to the label y. The active party naturally takes the responsibility as a dominating server in federated learning.

• Passive Party

  We define the data provider which has only a data matrix as a passive party. Passive parties play the role of clients in the federated learning setting. They are also in need of building a model to predict the class label y for their prediction purposes. Thus they must collaborate with the active party to build their model to predict y for their future users using their own features.

We align the data samples under an encryption scheme by using the privacy-preserving protocol for inter-database intersections to find the common shared users or data samples across the parties without compromising the non-shared parts of the user sets.

To ensure security, passive parties cannot get access to gradient and hessian directly. We use a "XGBoost" like tree-learning algorithm. In order to keep gradient and hessian confidential, we require that the active party encrypt gradient and hessian before sending them to passive parties. After encrypted the gradient and hessian, active party will send the encrypted [gradient] and [hessian] to passive party. Each passive party uses [gradient] and [hessian] to calculate the encrypted feature histograms, then encodes the (feature, split_bin_val) and constructs a (feature, split_bin_val) lookup table; it then sends the encoded value of (feature, split_bin_val) with feature histograms to the active party. After receiving the feature histograms from passive parties, the active party decrypts them and finds the best gains. If the best-gain feature belongs to a passive party, the active party sends the encoded (feature, split_bin_val) to back to the owner party. The following figure shows the process of finding split in federated tree building.

The parties continue the split finding process until tree construction finishes. Each party only knows the detailed split information of the tree nodes where the split features are provided by the party. The following figure shows the final structure of a single decision tree.

To use the learned model to classify a new instance, the active party first judges where current tree node belongs to. If the current tree belongs to the active party, then it can use its (feature, split_bin_val) lookup table to decide whether going to left child node or right; otherwise, the active party sends the node id to designated passive party, the passive party checks its lookup table and sends back which branch should the current node goes to. This process stops until the current node is a leave. The following figure shows the federated inference process.

By following the SecureBoost framework, multiple parties can jointly build tree ensemble model without leaking privacy in federated learning. If you want to learn more about the algorithm, you can read the paper attached above.

Optimization in Parallel Learning

SecureBoost uses data parallel learning algorithm to build the decision trees in every party. The procedure of the data parallel algorithm in each party is:

• Every party use mapPartitions API interface to generate feature-histograms of each partition of data.
• Use reduce API interface to merge global histograms from all local feature-histograms
• Find the best splits from merged global histograms by federated learning, then perform splits.

Applications

Hetero SecureBoost supports the following applications.

• binary classification, the objective function is sigmoid cross-entropy
• multi classification, the objective function is softmax cross-entropy
• regression, objective function includes least-squared-error-loss、least-absolutely-error-loss、huber-loss、 tweedie-loss、fair-loss、 log-cosh-loss

Other features

• Column sub-sample
• Controlling the number of nodes to split parallelly at each layer by setting max_split_nodes parameter，in order to avoid memory limit exceeding
• Support feature importance calculation
• Support Multi-host and single guest to build model
• Support different encrypt-mode to balance speed and security
• Support missing value in train and predict process
• Support evaluate training and validate data during training process
• Support another homomorphic encryption method called "Iterative
• Support early stopping in FATE-1.4, to use early stopping, see Boosting Tree Param
• Support sparse data optimization in FATE-1.5. You can activate it by setting "sparse_optimization" as true in conf. Notice that this feature may increase memory consumption. See here.
• Support feature subsample random seed setting in FATE-1.5
• Support feature binning error setting
• Support GOSS sampling in FATE-1.6
• Support cipher compressing and g, h packing in FATE-1.7

Homo SecureBoost

Unlike Hetero Secureboost, Homo SecureBoost is conducted under a different setting. In homo SecureBoost, every participant(clients) holds data that shares the same feature space, and jointly train a GBDT model without leaking any data sample.

The figure below shows the overall framework of the homo SecureBoost algorithm.

• Client
  Clients are the participants who hold their labeled samples. Samples from all client parties have the same feature space. Clients are to build a more powerful model together without leaking local samples, and they share the same trained model after learning.

• Server
  There are potentials of data leakage if all participants send its local histogram(which contains sum of gradient and hessian) to each other because sometimes features and labels can be inferred from gradient sums and hessian sums. Thus, to ensure security in the learning process, the Server uses secure aggregation to aggregate all participants' local histograms in a safe manner. The server can get a global histogram while not getting any local histogram and then find and broadcast the best splits to clients. Server collaborates with all clients in the learning process.

The key steps of learning a Homo SecureBoost model are described below:

1. Clients and Server initialize local settings. Clients and Server apply homo feature binning to get binning points for all features and then to pre-process local samples.

2. Clients and Server build a decision tree collaboratively:

   a. Clients compute local histograms for cur leaf nodes (left nodes or root node)

   b. The server applies secure aggregations: every local histogram plus a random number, and these numbers can cancel each other out. By this way server can get the global histogram without knowing any local histograms and data leakage is prevented. Figure below shows how histogram secure aggregations are conducted.

   

   c. The server commit histogram subtractions: getting the right node histograms by subtracting left node local histogram from parent node histogram. Then, the server will find the best splits points and broadcast them to clients.

   d. After getting the best split points, clients build the next layer for the current decision tree and re-assign samples. If current decision tree reaches the max depth or stop conditions are fulfilled, stop build the current tree, else go back to step (1). Figure below shows the procedure of fitting a decision tree.

   

3. If tree number reaches the max number, or loss is converged, Homo SecureBoost Fitting process stops.

By following the steps above clients are able to jointly build a GBDT model. Every client can then conduct inference on a new instance locally.

Optimization in learning

Homo SecureBoost utilizes data parallelization and histogram subtraction to accelerate the learning process.

• Every party use mapPartitions and reduce API interface to generate local feature-histograms, only samples in left nodes are used in computing feature histograms.
• The server aggregates all local histograms to get global histograms then get sibling histograms by subtracting left node histograms from parent histograms.
• The server finds the best splits from merged global histograms, then broadcast best splits.
• The computational cost and transmission cost are halved by using node subtraction.

Applications

Homo SecureBoost supports the following applications:

• binary classification, the objective function is sigmoid cross-entropy
• multi classification, the objective function is softmax cross-entropy
• regression, objective function includes least-squared-error-loss, least-absolutely-error-loss, huber-loss, tweedie-loss, fair-loss, log-cosh-loss

Other features

• Server uses safe aggregations to aggregate clients' histograms and losses, ensuring data security
• Column sub-sample
• Controlling the number of nodes to split parallelly at each layer by setting max_split_nodes parameter，in order to avoid memory limit exceeding
• Support feature importance calculation
• Support Multi-host and single guest to build model
• Support missing value in train and predict process
• Support evaluate training and validate data during training process
• Support feature subsample random seed setting in FATE-1.5
• Support feature binning error setting.

Param

boosting_param

Attributes

hetero_deprecated_param_list = ['early_stopping_rounds', 'validation_freqs', 'metrics', 'use_first_metric_only'] module-attribute

homo_deprecated_param_list = ['validation_freqs', 'metrics'] module-attribute

Classes

ObjectiveParam(objective='cross_entropy', params=None)

Bases: BaseParam

Define objective parameters that used in federated ml.

Parameters:

Name	Type	Description	Default
objective		None in host's config, should be str in guest'config. when task_type is classification, only support 'cross_entropy', other 6 types support in regression task	None
params	None or list	should be non empty list when objective is 'tweedie','fair','huber', first element of list shoulf be a float-number large than 0.0 when objective is 'fair', 'huber', first element of list should be a float-number in [1.0, 2.0) when objective is 'tweedie'	None

Source code in python/federatedml/param/boosting_param.py

def __init__(self, objective='cross_entropy', params=None):
    self.objective = objective
    self.params = params

Attributes

objective = objective instance-attribute

params = params instance-attribute

Functions

check(task_type=None)

Source code in python/federatedml/param/boosting_param.py

def check(self, task_type=None):
    if self.objective is None:
        return True

    descr = "objective param's"

    LOGGER.debug('check objective {}'.format(self.objective))

    if task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
        self.objective = self.check_and_change_lower(self.objective,
                                                     ["cross_entropy", "lse", "lae", "huber", "fair",
                                                      "log_cosh", "tweedie"],
                                                     descr)

    if task_type == consts.CLASSIFICATION:
        if self.objective != "cross_entropy":
            raise ValueError("objective param's objective {} not supported".format(self.objective))

    elif task_type == consts.REGRESSION:
        self.objective = self.check_and_change_lower(self.objective,
                                                     ["lse", "lae", "huber", "fair", "log_cosh", "tweedie"],
                                                     descr)

        params = self.params
        if self.objective in ["huber", "fair", "tweedie"]:
            if type(params).__name__ != 'list' or len(params) < 1:
                raise ValueError(
                    "objective param's params {} not supported, should be non-empty list".format(params))

            if type(params[0]).__name__ not in ["float", "int", "long"]:
                raise ValueError("objective param's params[0] {} not supported".format(self.params[0]))

            if self.objective == 'tweedie':
                if params[0] < 1 or params[0] >= 2:
                    raise ValueError("in tweedie regression, objective params[0] should betweend [1, 2)")

            if self.objective == 'fair' or 'huber':
                if params[0] <= 0.0:
                    raise ValueError("in {} regression, objective params[0] should greater than 0.0".format(
                        self.objective))
    return True

DecisionTreeParam(criterion_method='xgboost', criterion_params=[0.1, 0], max_depth=3, min_sample_split=2, min_impurity_split=0.001, min_leaf_node=1, max_split_nodes=consts.MAX_SPLIT_NODES, feature_importance_type='split', n_iter_no_change=True, tol=0.001, min_child_weight=0, use_missing=False, zero_as_missing=False, deterministic=False)

Bases: BaseParam

Define decision tree parameters that used in federated ml.

Parameters:

Name	Type	Description	Default
criterion_method		the criterion function to use	"xgboost"
criterion_params		should be non empty and elements are float-numbers, if a list is offered, the first one is l2 regularization value, and the second one is l1 regularization value. if a dict is offered, make sure it contains key 'l1', and 'l2'. l1, l2 regularization values are non-negative floats. default: [0.1, 0] or {'l1':0, 'l2':0,1}	[0.1, 0]
max_depth		the max depth of a decision tree, default: 3	3
min_sample_split		least quantity of nodes to split, default: 2	2
min_impurity_split		least gain of a single split need to reach, default: 1e-3	0.001
min_child_weight		sum of hessian needed in child nodes. default is 0	0
min_leaf_node		when samples no more than min_leaf_node, it becomes a leave, default: 1	1
max_split_nodes		we will use no more than max_split_nodes to parallel finding their splits in a batch, for memory consideration. default is 65536	consts.MAX_SPLIT_NODES
feature_importance_type		if is 'split', feature_importances calculate by feature split times, if is 'gain', feature_importances calculate by feature split gain. default: 'split' Due to the safety concern, we adjust training strategy of Hetero-SBT in FATE-1.8, When running Hetero-SBT, this parameter is now abandoned. In Hetero-SBT of FATE-1.8, guest side will compute split, gain of local features, and receive anonymous feature importance results from hosts. Hosts will compute split importance of local features.	'split'
use_missing		use missing value in training process or not.	False
zero_as_missing		regard 0 as missing value or not, will be use only if use_missing=True, default: False	False
deterministic		ensure stability when computing histogram. Set this to true to ensure stable result when using same data and same parameter. But it may slow down computation.	False

Source code in python/federatedml/param/boosting_param.py

def __init__(self, criterion_method="xgboost", criterion_params=[0.1, 0], max_depth=3,
             min_sample_split=2, min_impurity_split=1e-3, min_leaf_node=1,
             max_split_nodes=consts.MAX_SPLIT_NODES, feature_importance_type='split',
             n_iter_no_change=True, tol=0.001, min_child_weight=0,
             use_missing=False, zero_as_missing=False, deterministic=False):

    super(DecisionTreeParam, self).__init__()

    self.criterion_method = criterion_method
    self.criterion_params = criterion_params
    self.max_depth = max_depth
    self.min_sample_split = min_sample_split
    self.min_impurity_split = min_impurity_split
    self.min_leaf_node = min_leaf_node
    self.min_child_weight = min_child_weight
    self.max_split_nodes = max_split_nodes
    self.feature_importance_type = feature_importance_type
    self.n_iter_no_change = n_iter_no_change
    self.tol = tol
    self.use_missing = use_missing
    self.zero_as_missing = zero_as_missing
    self.deterministic = deterministic

Attributes

criterion_method = criterion_method instance-attribute

criterion_params = criterion_params instance-attribute

max_depth = max_depth instance-attribute

min_sample_split = min_sample_split instance-attribute

min_impurity_split = min_impurity_split instance-attribute

min_leaf_node = min_leaf_node instance-attribute

min_child_weight = min_child_weight instance-attribute

max_split_nodes = max_split_nodes instance-attribute

feature_importance_type = feature_importance_type instance-attribute

n_iter_no_change = n_iter_no_change instance-attribute

tol = tol instance-attribute

use_missing = use_missing instance-attribute

zero_as_missing = zero_as_missing instance-attribute

deterministic = deterministic instance-attribute

Functions

check()

Source code in python/federatedml/param/boosting_param.py

def check(self):
    descr = "decision tree param"

    self.criterion_method = self.check_and_change_lower(self.criterion_method,
                                                        ["xgboost"],
                                                        descr)

    if len(self.criterion_params) == 0:
        raise ValueError("decisition tree param's criterio_params should be non empty")

    if isinstance(self.criterion_params, list):
        assert len(self.criterion_params) == 2, 'length of criterion_param should be 2: l1, l2 regularization ' \
                                                'values are needed'
        self.check_nonnegative_number(self.criterion_params[0], 'l2 reg value')
        self.check_nonnegative_number(self.criterion_params[1], 'l1 reg value')

    elif isinstance(self.criterion_params, dict):
        assert 'l1' in self.criterion_params and 'l2' in self.criterion_params, 'l1 and l2 keys are needed in ' \
                                                                                'criterion_params dict'
        self.criterion_params = [self.criterion_params['l2'], self.criterion_params['l1']]
    else:
        raise ValueError('criterion_params should be a dict or a list contains l1, l2 reg value')

    if type(self.max_depth).__name__ not in ["int", "long"]:
        raise ValueError("decision tree param's max_depth {} not supported, should be integer".format(
            self.max_depth))

    if self.max_depth < 1:
        raise ValueError("decision tree param's max_depth should be positive integer, no less than 1")

    if type(self.min_sample_split).__name__ not in ["int", "long"]:
        raise ValueError("decision tree param's min_sample_split {} not supported, should be integer".format(
            self.min_sample_split))

    if type(self.min_impurity_split).__name__ not in ["int", "long", "float"]:
        raise ValueError("decision tree param's min_impurity_split {} not supported, should be numeric".format(
            self.min_impurity_split))

    if type(self.min_leaf_node).__name__ not in ["int", "long"]:
        raise ValueError("decision tree param's min_leaf_node {} not supported, should be integer".format(
            self.min_leaf_node))

    if type(self.max_split_nodes).__name__ not in ["int", "long"] or self.max_split_nodes < 1:
        raise ValueError("decision tree param's max_split_nodes {} not supported, " +
                         "should be positive integer between 1 and {}".format(self.max_split_nodes,
                                                                              consts.MAX_SPLIT_NODES))

    if type(self.n_iter_no_change).__name__ != "bool":
        raise ValueError("decision tree param's n_iter_no_change {} not supported, should be bool type".format(
            self.n_iter_no_change))

    if type(self.tol).__name__ not in ["float", "int", "long"]:
        raise ValueError("decision tree param's tol {} not supported, should be numeric".format(self.tol))

    self.feature_importance_type = self.check_and_change_lower(self.feature_importance_type,
                                                               ["split", "gain"],
                                                               descr)
    self.check_nonnegative_number(self.min_child_weight, 'min_child_weight')
    self.check_boolean(self.deterministic, 'deterministic')

    return True

BoostingParam(task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, metrics=None, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR)

Bases: BaseParam

Basic parameter for Boosting Algorithms

Parameters:

Name	Type	Description	Default
task_type		task type	'classification'
objective_param	ObjectiveParam Object, default	objective param	ObjectiveParam()
learning_rate	float, int or long	the learning rate of secure boost. default: 0.3	0.3
num_trees	int or float	the max number of boosting round. default: 5	5
subsample_feature_rate	float	a float-number in [0, 1], default: 1.0	1
n_iter_no_change	bool	when True and residual error less than tol, tree building process will stop. default: True	True
bin_num		bin number use in quantile. default: 32	32
validation_freqs		Do validation in training process or Not. if equals None, will not do validation in train process; if equals positive integer, will validate data every validation_freqs epochs passes; if container object in python, will validate data if epochs belong to this container. e.g. validation_freqs = [10, 15], will validate data when epoch equals to 10 and 15. Default: None	None

Source code in python/federatedml/param/boosting_param.py

def __init__(self, task_type=consts.CLASSIFICATION,
             objective_param=ObjectiveParam(),
             learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True,
             tol=0.0001, bin_num=32,
             predict_param=PredictParam(), cv_param=CrossValidationParam(),
             validation_freqs=None, metrics=None, random_seed=100,
             binning_error=consts.DEFAULT_RELATIVE_ERROR):

    super(BoostingParam, self).__init__()

    self.task_type = task_type
    self.objective_param = copy.deepcopy(objective_param)
    self.learning_rate = learning_rate
    self.num_trees = num_trees
    self.subsample_feature_rate = subsample_feature_rate
    self.n_iter_no_change = n_iter_no_change
    self.tol = tol
    self.bin_num = bin_num
    self.predict_param = copy.deepcopy(predict_param)
    self.cv_param = copy.deepcopy(cv_param)
    self.validation_freqs = validation_freqs
    self.metrics = metrics
    self.random_seed = random_seed
    self.binning_error = binning_error

Attributes

task_type = task_type instance-attribute

objective_param = copy.deepcopy(objective_param) instance-attribute

learning_rate = learning_rate instance-attribute

num_trees = num_trees instance-attribute

subsample_feature_rate = subsample_feature_rate instance-attribute

n_iter_no_change = n_iter_no_change instance-attribute

tol = tol instance-attribute

bin_num = bin_num instance-attribute

predict_param = copy.deepcopy(predict_param) instance-attribute

cv_param = copy.deepcopy(cv_param) instance-attribute

validation_freqs = validation_freqs instance-attribute

metrics = metrics instance-attribute

random_seed = random_seed instance-attribute

binning_error = binning_error instance-attribute

Functions

check()

Source code in python/federatedml/param/boosting_param.py

def check(self):

    descr = "boosting tree param's"

    if self.task_type not in [consts.CLASSIFICATION, consts.REGRESSION]:
        raise ValueError("boosting_core tree param's task_type {} not supported, should be {} or {}".format(
            self.task_type, consts.CLASSIFICATION, consts.REGRESSION))

    self.objective_param.check(self.task_type)

    if type(self.learning_rate).__name__ not in ["float", "int", "long"]:
        raise ValueError("boosting_core tree param's learning_rate {} not supported, should be numeric".format(
            self.learning_rate))

    if type(self.subsample_feature_rate).__name__ not in ["float", "int", "long"] or \
            self.subsample_feature_rate < 0 or self.subsample_feature_rate > 1:
        raise ValueError(
            "boosting_core tree param's subsample_feature_rate should be a numeric number between 0 and 1")

    if type(self.n_iter_no_change).__name__ != "bool":
        raise ValueError("boosting_core tree param's n_iter_no_change {} not supported, should be bool type".format(
            self.n_iter_no_change))

    if type(self.tol).__name__ not in ["float", "int", "long"]:
        raise ValueError("boosting_core tree param's tol {} not supported, should be numeric".format(self.tol))

    if type(self.bin_num).__name__ not in ["int", "long"] or self.bin_num < 2:
        raise ValueError(
            "boosting_core tree param's bin_num {} not supported, should be positive integer greater than 1".format(
                self.bin_num))

    if self.validation_freqs is None:
        pass
    elif isinstance(self.validation_freqs, int):
        if self.validation_freqs < 1:
            raise ValueError("validation_freqs should be larger than 0 when it's integer")
    elif not isinstance(self.validation_freqs, collections.Container):
        raise ValueError("validation_freqs should be None or positive integer or container")

    if self.metrics is not None and not isinstance(self.metrics, list):
        raise ValueError("metrics should be a list")

    if self.random_seed is not None:
        assert isinstance(self.random_seed, int) and self.random_seed >= 0, 'random seed must be an integer >= 0'

    self.check_decimal_float(self.binning_error, descr)

    return True

HeteroBoostingParam(task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, encrypt_param=EncryptParam(), bin_num=32, encrypted_mode_calculator_param=EncryptedModeCalculatorParam(), predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR)

Bases: BoostingParam

Parameters:

Name	Type	Description	Default
encrypt_param	EncodeParam Object	encrypt method use in secure boost, default: EncryptParam()	EncryptParam()
encrypted_mode_calculator_param		the calculation mode use in secureboost, default: EncryptedModeCalculatorParam()	EncryptedModeCalculatorParam()

Source code in python/federatedml/param/boosting_param.py

def __init__(self, task_type=consts.CLASSIFICATION,
             objective_param=ObjectiveParam(),
             learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True,
             tol=0.0001, encrypt_param=EncryptParam(),
             bin_num=32,
             encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
             predict_param=PredictParam(), cv_param=CrossValidationParam(),
             validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False,
             random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR):

    super(HeteroBoostingParam, self).__init__(task_type, objective_param, learning_rate, num_trees,
                                              subsample_feature_rate, n_iter_no_change, tol, bin_num,
                                              predict_param, cv_param, validation_freqs, metrics=metrics,
                                              random_seed=random_seed,
                                              binning_error=binning_error)

    self.encrypt_param = copy.deepcopy(encrypt_param)
    self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
    self.early_stopping_rounds = early_stopping_rounds
    self.use_first_metric_only = use_first_metric_only

Attributes

encrypt_param = copy.deepcopy(encrypt_param) instance-attribute

encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param) instance-attribute

early_stopping_rounds = early_stopping_rounds instance-attribute

use_first_metric_only = use_first_metric_only instance-attribute

Functions

check()

Source code in python/federatedml/param/boosting_param.py

def check(self):

    super(HeteroBoostingParam, self).check()
    self.encrypted_mode_calculator_param.check()
    self.encrypt_param.check()

    if self.early_stopping_rounds is None:
        pass
    elif isinstance(self.early_stopping_rounds, int):
        if self.early_stopping_rounds < 1:
            raise ValueError("early stopping rounds should be larger than 0 when it's integer")
        if self.validation_freqs is None:
            raise ValueError("validation freqs must be set when early stopping is enabled")

    if not isinstance(self.use_first_metric_only, bool):
        raise ValueError("use_first_metric_only should be a boolean")

    return True

HeteroSecureBoostParam(tree_param=DecisionTreeParam(), task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1.0, n_iter_no_change=True, tol=0.0001, encrypt_param=EncryptParam(), bin_num=32, encrypted_mode_calculator_param=EncryptedModeCalculatorParam(), predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, early_stopping_rounds=None, use_missing=False, zero_as_missing=False, complete_secure=0, metrics=None, use_first_metric_only=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR, sparse_optimization=False, run_goss=False, top_rate=0.2, other_rate=0.1, cipher_compress_error=None, cipher_compress=True, new_ver=True, boosting_strategy=consts.STD_TREE, work_mode=None, tree_num_per_party=1, guest_depth=2, host_depth=3, callback_param=CallbackParam(), multi_mode=consts.SINGLE_OUTPUT, EINI_inference=False, EINI_random_mask=False, EINI_complexity_check=False)

Bases: HeteroBoostingParam

Define boosting tree parameters that used in federated ml.

Parameters:

Name	Type	Description	Default
task_type		task type	'classification'
tree_param	DecisionTreeParam Object, default	tree param	DecisionTreeParam()
objective_param	ObjectiveParam Object, default	objective param	ObjectiveParam()
learning_rate	float, int or long	the learning rate of secure boost. default: 0.3	0.3
num_trees	int or float	the max number of trees to build. default: 5	5
subsample_feature_rate	float	a float-number in [0, 1], default: 1.0	1.0
random_seed		seed that controls all random functions	100
n_iter_no_change	bool	when True and residual error less than tol, tree building process will stop. default: True	True
encrypt_param	EncodeParam Object	encrypt method use in secure boost, default: EncryptParam(), this parameter is only for hetero-secureboost	EncryptParam()
bin_num		bin number use in quantile. default: 32	32
encrypted_mode_calculator_param		the calculation mode use in secureboost, default: EncryptedModeCalculatorParam(), only for hetero-secureboost	EncryptedModeCalculatorParam()
use_missing		use missing value in training process or not. default: False	False
zero_as_missing		regard 0 as missing value or not, will be use only if use_missing=True, default: False	False
validation_freqs		Do validation in training process or Not. if equals None, will not do validation in train process; if equals positive integer, will validate data every validation_freqs epochs passes; if container object in python, will validate data if epochs belong to this container. e.g. validation_freqs = [10, 15], will validate data when epoch equals to 10 and 15. Default: None The default value is None, 1 is suggested. You can set it to a number larger than 1 in order to speed up training by skipping validation rounds. When it is larger than 1, a number which is divisible by "num_trees" is recommended, otherwise, you will miss the validation scores of last training iteration.	None
early_stopping_rounds		will stop training if one metric of one validation data doesn’t improve in last early_stopping_round rounds， need to set validation freqs and will check early_stopping every at every validation epoch,	None
metrics		Specify which metrics to be used when performing evaluation during training process. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']， For binary-classificatiin tasks, default metrics are ['auc', 'ks']. For multi-classification tasks, default metrics are ['accuracy', 'precision', 'recall']	None
use_first_metric_only		use only the first metric for early stopping	False
complete_secure		if use complete_secure, when use complete secure, build first 'complete secure' tree using only guest features	0
sparse_optimization		this parameter is abandoned in FATE-1.7.1	False
run_goss		activate Gradient-based One-Side Sampling, which selects large gradient and small gradient samples using top_rate and other_rate.	False
top_rate			0.2
other_rate			0.1
cipher_compress_error			None
cipher_compress			True
boosting_strategy		use guest features, the second k trees use host features, and so on layered: only support 2 party, when running layered mode, first 'host_depth' layer will use host features, and then next 'guest_depth' will only use guest features	consts.STD_TREE
work_mode		This parameter has the same function as boosting_strategy, but is deprecated	None
tree_num_per_party		param is valid when boosting_strategy is mix	1
guest_depth		is layered	2
host_depth		layered	3
multi_mode		single_output standard gbdt multi-classification strategy multi_output every leaf give a multi-dimension predict, using multi_mode can save time by learning a model with less trees.	consts.SINGLE_OUTPUT
EINI_inference		default is False, this option changes the inference algorithm used in predict tasks. a secure prediction method that hides decision path to enhance security in the inference step. This method is insprired by EINI inference algorithm.	False
EINI_random_mask		default is False multiply predict result by a random float number to confuse original predict result. This operation further enhances the security of naive EINI algorithm.	False
EINI_complexity_check		default is False check the complexity of tree models when running EINI algorithms. Complexity models are easy to hide their decision path, while simple tree models are not, therefore if a tree model is too simple, it is not allowed to run EINI predict algorithms.	False

Source code in python/federatedml/param/boosting_param.py

def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION,
             objective_param=ObjectiveParam(),
             learning_rate=0.3, num_trees=5, subsample_feature_rate=1.0, n_iter_no_change=True,
             tol=0.0001, encrypt_param=EncryptParam(),
             bin_num=32,
             encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
             predict_param=PredictParam(), cv_param=CrossValidationParam(),
             validation_freqs=None, early_stopping_rounds=None, use_missing=False, zero_as_missing=False,
             complete_secure=0, metrics=None, use_first_metric_only=False, random_seed=100,
             binning_error=consts.DEFAULT_RELATIVE_ERROR,
             sparse_optimization=False, run_goss=False, top_rate=0.2, other_rate=0.1,
             cipher_compress_error=None, cipher_compress=True, new_ver=True, boosting_strategy=consts.STD_TREE,
             work_mode=None, tree_num_per_party=1, guest_depth=2, host_depth=3, callback_param=CallbackParam(),
             multi_mode=consts.SINGLE_OUTPUT, EINI_inference=False, EINI_random_mask=False,
             EINI_complexity_check=False):

    super(HeteroSecureBoostParam, self).__init__(task_type, objective_param, learning_rate, num_trees,
                                                 subsample_feature_rate, n_iter_no_change, tol, encrypt_param,
                                                 bin_num, encrypted_mode_calculator_param, predict_param, cv_param,
                                                 validation_freqs, early_stopping_rounds, metrics=metrics,
                                                 use_first_metric_only=use_first_metric_only,
                                                 random_seed=random_seed,
                                                 binning_error=binning_error)

    self.tree_param = copy.deepcopy(tree_param)
    self.zero_as_missing = zero_as_missing
    self.use_missing = use_missing
    self.complete_secure = complete_secure
    self.sparse_optimization = sparse_optimization
    self.run_goss = run_goss
    self.top_rate = top_rate
    self.other_rate = other_rate
    self.cipher_compress_error = cipher_compress_error
    self.cipher_compress = cipher_compress
    self.new_ver = new_ver
    self.EINI_inference = EINI_inference
    self.EINI_random_mask = EINI_random_mask
    self.EINI_complexity_check = EINI_complexity_check
    self.boosting_strategy = boosting_strategy
    self.work_mode = work_mode
    self.tree_num_per_party = tree_num_per_party
    self.guest_depth = guest_depth
    self.host_depth = host_depth
    self.callback_param = copy.deepcopy(callback_param)
    self.multi_mode = multi_mode

Attributes

tree_param = copy.deepcopy(tree_param) instance-attribute

zero_as_missing = zero_as_missing instance-attribute

use_missing = use_missing instance-attribute

complete_secure = complete_secure instance-attribute

sparse_optimization = sparse_optimization instance-attribute

run_goss = run_goss instance-attribute

top_rate = top_rate instance-attribute

other_rate = other_rate instance-attribute

cipher_compress_error = cipher_compress_error instance-attribute

cipher_compress = cipher_compress instance-attribute

new_ver = new_ver instance-attribute

EINI_inference = EINI_inference instance-attribute

EINI_random_mask = EINI_random_mask instance-attribute

EINI_complexity_check = EINI_complexity_check instance-attribute

boosting_strategy = boosting_strategy instance-attribute

work_mode = work_mode instance-attribute

tree_num_per_party = tree_num_per_party instance-attribute

guest_depth = guest_depth instance-attribute

host_depth = host_depth instance-attribute

callback_param = copy.deepcopy(callback_param) instance-attribute

multi_mode = multi_mode instance-attribute

Functions

check()

Source code in python/federatedml/param/boosting_param.py

def check(self):

    super(HeteroSecureBoostParam, self).check()
    self.tree_param.check()
    if not isinstance(self.use_missing, bool):
        raise ValueError('use missing should be bool type')
    if not isinstance(self.zero_as_missing, bool):
        raise ValueError('zero as missing should be bool type')
    self.check_boolean(self.run_goss, 'run goss')
    self.check_decimal_float(self.top_rate, 'top rate')
    self.check_decimal_float(self.other_rate, 'other rate')
    self.check_positive_number(self.other_rate, 'other_rate')
    self.check_positive_number(self.top_rate, 'top_rate')
    self.check_boolean(self.new_ver, 'code version switcher')
    self.check_boolean(self.cipher_compress, 'cipher compress')
    self.check_boolean(self.EINI_inference, 'eini inference')
    self.check_boolean(self.EINI_random_mask, 'eini random mask')
    self.check_boolean(self.EINI_complexity_check, 'eini complexity check')

    assert isinstance(self.complete_secure,
                      int) and self.complete_secure >= 0, "complete secure should be an int >= 0"

    if self.EINI_inference and self.EINI_random_mask:
        LOGGER.warning('To protect the inference decision path, notice that current setting will multiply'
                       ' predict result by a random number, hence SecureBoost will return confused predict scores'
                       ' that is not the same as the original predict scores')

    if self.work_mode == consts.MIX_TREE and self.EINI_inference:
        LOGGER.warning('Mix tree mode does not support EINI, use default predict setting')

    if self.work_mode is not None:
        self.boosting_strategy = self.work_mode

    if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
        raise ValueError('unsupported multi-classification mode')
    if self.multi_mode == consts.MULTI_OUTPUT:
        if self.boosting_strategy != consts.STD_TREE:
            raise ValueError('MO trees only works when boosting strategy is std tree')
        if not self.cipher_compress:
            raise ValueError('Mo trees only works when cipher compress is enabled')

    if self.boosting_strategy not in [consts.STD_TREE, consts.LAYERED_TREE, consts.MIX_TREE]:
        raise ValueError('unknown sbt boosting strategy{}'.format(self.boosting_strategy))

    for p in ["early_stopping_rounds", "validation_freqs", "metrics",
              "use_first_metric_only"]:
        # if self._warn_to_deprecate_param(p, "", ""):
        if self._deprecated_params_set.get(p):
            if "callback_param" in self.get_user_feeded():
                raise ValueError(f"{p} and callback param should not be set simultaneously，"
                                 f"{self._deprecated_params_set}, {self.get_user_feeded()}")
            else:
                self.callback_param.callbacks = ["PerformanceEvaluate"]
            break

    descr = "boosting_param's"

    if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"):
        self.callback_param.validation_freqs = self.validation_freqs

    if self._warn_to_deprecate_param("early_stopping_rounds", descr, "callback_param's 'early_stopping_rounds'"):
        self.callback_param.early_stopping_rounds = self.early_stopping_rounds

    if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"):
        self.callback_param.metrics = self.metrics

    if self._warn_to_deprecate_param("use_first_metric_only", descr, "callback_param's 'use_first_metric_only'"):
        self.callback_param.use_first_metric_only = self.use_first_metric_only

    if self.top_rate + self.other_rate >= 1:
        raise ValueError('sum of top rate and other rate should be smaller than 1')

    return True

HomoSecureBoostParam(tree_param=DecisionTreeParam(), task_type=consts.CLASSIFICATION, objective_param=ObjectiveParam(), learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True, tol=0.0001, bin_num=32, predict_param=PredictParam(), cv_param=CrossValidationParam(), validation_freqs=None, use_missing=False, zero_as_missing=False, random_seed=100, binning_error=consts.DEFAULT_RELATIVE_ERROR, backend=consts.DISTRIBUTED_BACKEND, callback_param=CallbackParam(), multi_mode=consts.SINGLE_OUTPUT)

Bases: BoostingParam

Parameters:

Name	Type	Description	Default
backend		decides which backend to use when computing histograms for homo-sbt	consts.DISTRIBUTED_BACKEND

Source code in python/federatedml/param/boosting_param.py

def __init__(self, tree_param: DecisionTreeParam = DecisionTreeParam(), task_type=consts.CLASSIFICATION,
             objective_param=ObjectiveParam(),
             learning_rate=0.3, num_trees=5, subsample_feature_rate=1, n_iter_no_change=True,
             tol=0.0001, bin_num=32, predict_param=PredictParam(), cv_param=CrossValidationParam(),
             validation_freqs=None, use_missing=False, zero_as_missing=False, random_seed=100,
             binning_error=consts.DEFAULT_RELATIVE_ERROR, backend=consts.DISTRIBUTED_BACKEND,
             callback_param=CallbackParam(), multi_mode=consts.SINGLE_OUTPUT):

    super(HomoSecureBoostParam, self).__init__(task_type=task_type,
                                               objective_param=objective_param,
                                               learning_rate=learning_rate,
                                               num_trees=num_trees,
                                               subsample_feature_rate=subsample_feature_rate,
                                               n_iter_no_change=n_iter_no_change,
                                               tol=tol,
                                               bin_num=bin_num,
                                               predict_param=predict_param,
                                               cv_param=cv_param,
                                               validation_freqs=validation_freqs,
                                               random_seed=random_seed,
                                               binning_error=binning_error
                                               )
    self.use_missing = use_missing
    self.zero_as_missing = zero_as_missing
    self.tree_param = copy.deepcopy(tree_param)
    self.backend = backend
    self.callback_param = copy.deepcopy(callback_param)
    self.multi_mode = multi_mode

Attributes

use_missing = use_missing instance-attribute

zero_as_missing = zero_as_missing instance-attribute

tree_param = copy.deepcopy(tree_param) instance-attribute

backend = backend instance-attribute

callback_param = copy.deepcopy(callback_param) instance-attribute

multi_mode = multi_mode instance-attribute

Functions

check()

Source code in python/federatedml/param/boosting_param.py

def check(self):

    super(HomoSecureBoostParam, self).check()
    self.tree_param.check()
    if not isinstance(self.use_missing, bool):
        raise ValueError('use missing should be bool type')
    if not isinstance(self.zero_as_missing, bool):
        raise ValueError('zero as missing should be bool type')
    if self.backend not in [consts.MEMORY_BACKEND, consts.DISTRIBUTED_BACKEND]:
        raise ValueError('unsupported backend')
    if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
        raise ValueError('unsupported multi-classification mode')

    for p in ["validation_freqs", "metrics"]:
        # if self._warn_to_deprecate_param(p, "", ""):
        if self._deprecated_params_set.get(p):
            if "callback_param" in self.get_user_feeded():
                raise ValueError(f"{p} and callback param should not be set simultaneously，"
                                 f"{self._deprecated_params_set}, {self.get_user_feeded()}")
            else:
                self.callback_param.callbacks = ["PerformanceEvaluate"]
            break

    descr = "boosting_param's"

    if self._warn_to_deprecate_param("validation_freqs", descr, "callback_param's 'validation_freqs'"):
        self.callback_param.validation_freqs = self.validation_freqs

    if self._warn_to_deprecate_param("metrics", descr, "callback_param's 'metrics'"):
        self.callback_param.metrics = self.metrics

    if self.multi_mode not in [consts.SINGLE_OUTPUT, consts.MULTI_OUTPUT]:
        raise ValueError('unsupported multi-classification mode')

    if self.multi_mode == consts.MULTI_OUTPUT:
        if self.task_type == consts.REGRESSION:
            raise ValueError('regression tasks not support multi-output trees')

    return True

Functions

Hetero Complete Secureboost

Now Hetero SecureBoost adds a new option: complete_secure. Once enabled, the boosting model will only use guest features to build the first decision tree. This can avoid label leakages, accord to SecureBoost: A Lossless Federated Learning Framework.

Examples

Example

Pipeline

DSL

## Hetero SecureBoost Configuration Usage Guide.

#### Example Tasks.

1. Binary-Class:  

    example-data: (1) guest: breast_hetero_guest.csv  (2) host: breast_hetero_host.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_binary_conf.json

2. Multi-Class:  

    example-data: (1) guest: vehicle_scale_hetero_guest.csv
                  (2) host: vehicle_scale_hetero_host.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_multi_conf.json

3. Regression:  

    example-data: (1) guest: student_hetero_guest.csv
                  (2) host: student_hetero_host.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_regression_conf.json

4. Multi-Host Regression  

    example-data: (1) guest: motor_hetero_guest.csv
                  (2) host1: motor_hetero_host_1.csv; 
                  (3) host2: motor_hetero_host_2.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_regression_multi_host_conf.json

5. Binary-Class With Missing Value  

    example-data: (1) guest: ionosphere_scale_hetero_guest.csv
                  (2) host: ionosphere_scale_hetero_host.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_binary_with_missing_value_conf.json  

    This example also contains another two feature since FATE-1.1.  
    (1) evaluate data during training process, check the "validation_freqs" field in runtime_config  

6. Early stopping example

    example-data: (1) guest: student_hetero_guest.csv
                  (2) host: student_hetero_host.csv  

    dsl: test_secureboost_train_dsl.json  

    runtime_config: test_secureboost_train_with_early_stopping_conf.json


#### Cross Validation Class

1. Binary-Class:  

    example-data: (1) guest: breast_hetero_guest.csv
                  (2) host: breast_hetero_guest.csv  

    dsl: test_secureboost_cross_validation_dsl.json  

    runtime_config: test_secureboost_cross_validation_binary_conf.json 

2. Multi-Class:  

    example-data: (1) guest: vehicle_scale_hetero_guest.csv
                  (2) host: vehicle_scal_a.csv  

    dsl: test_secureboost_cross_validation_binary_conf.json 

    runtime_config: test_secureboost_cross_validation_multi_conf.json  

3. Regression:  

    example-data: (1) guest: student_hetero_guest.csv
                  (2) host: student_hetero_host.csv  

    dsl: test_secureboost_cross_validation_dsl.json  

    runtime_config: test_secureboost_cross_validation_regression_conf.json

Users can use following commands to run a task.

    flow job submit -c ${runtime_config} -d ${dsl}

Moreover, after successfully running the training task, you can use it to predict too.

test_secureboost_train_complete_secure_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "complete_secure": true
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_cross_validation_dsl.json

{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_secure_boost_0": {
            "module": "HeteroSecureBoost",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        }
    }
}            

test_secureboost_train_regression_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "regression",
                "objective_param": {
                    "objective": "lse"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            },
            "evaluation_0": {
                "eval_type": "regression"
            }
        },
        "role": {
            "host": {
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "reader_1": {
                        "table": {
                            "name": "student_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true,
                        "label_type": "float",
                        "output_format": "dense"
                    },
                    "reader_1": {
                        "table": {
                            "name": "student_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "label_type": "float",
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_EINI_predict_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "job_parameters": {
        "common": {
            "model_id": "guest-10000#host-9999#model",
            "model_version": "20200928174750711017114",
            "job_type": "predict"
        }
    },
    "component_parameters": {
        "common": {"hetero_secure_boost_0":{"EINI_inference": true, "EINI_random_mask": true}},
        "role": {
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_train_multi_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            },
            "evaluation_0": {
                "eval_type": "multi"
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_train_dsl.json

{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "reader_1": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "data_transform_1": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_1.data"
                    ]
                },
                "model": [
                    "data_transform_0.model"
                ]
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "intersection_1": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_1.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_secure_boost_0": {
            "module": "HeteroSecureBoost",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ],
                    "validate_data": [
                        "intersection_1.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "evaluation_0": {
            "module": "Evaluation",
            "input": {
                "data": {
                    "data": [
                        "hetero_secure_boost_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        }
    }
}            

test_secureboost_warm_start_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "arbiter": [
            9999
        ],
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "role": {
            "host": {
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        },
        "common": {
            "data_transform_0": {
                "output_format": "dense"
            },
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            },
            "hetero_secure_boost_1": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "callback_param": {
                    "callbacks": [
                        "PerformanceEvaluate"
                    ],
                    "validation_freqs": 1
                }
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        }
    }
}            

test_secureboost_train_binary_with_missing_value_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "use_missing": true
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "ionosphere_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "label_name": "label",
                        "label_type": "int",
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "label_name": "label",
                        "label_type": "int",
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "ionosphere_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "ionosphere_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "ionosphere_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_cross_validation_regression_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "regression",
                "objective_param": {
                    "objective": "lse"
                },
                "num_trees": 3,
                "cv_param": {
                    "need_cv": true,
                    "n_splits": 5,
                    "shuffle": false,
                    "random_seed": 103
                },
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    }
                }
            },
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "label_type": "float",
                        "output_format": "dense"
                    }
                }
            }
        }
    }
}            

test_secureboost_cross_validation_binary_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "cv_param": {
                    "need_cv": true,
                    "n_splits": 5,
                    "shuffle": false,
                    "random_seed": 103
                },
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    }
                }
            }
        }
    }
}            

test_secureboost_train_layered_mode_binary_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "boosting_strategy": "layered"
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}

test_secureboost_warm_start_dsl.json

{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "cache": [
                    "cache"
                ]
            }
        },
        "hetero_secure_boost_0": {
            "module": "HeteroSecureBoost",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "hetero_secure_boost_1": {
            "module": "HeteroSecureBoost",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                },
                "model": [
                    "hetero_secure_boost_0.model"
                ]
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "evaluation_0": {
            "module": "Evaluation",
            "input": {
                "data": {
                    "data": [
                        "hetero_secure_boost_1.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        }
    }
}            

test_secureboost_train_with_early_stopping_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "regression",
                "objective_param": {
                    "objective": "lse"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "early_stopping_rounds": 1,
                "tree_param": {
                    "max_depth": 3
                }
            },
            "evaluation_0": {
                "eval_type": "regression"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_1": {
                        "table": {
                            "name": "student_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "data_transform_1": {
                        "with_label": false
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "student_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_1": {
                        "table": {
                            "name": "student_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_predict_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "job_parameters": {
        "common": {
            "model_id": "guest-10000#host-9999#model",
            "model_version": "20200928174750711017114",
            "job_type": "predict"
        }
    },
    "component_parameters": {
        "role": {
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_train_binary_cipher_compress_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "cipher_compress_error": 8
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}            

test_secureboost_train_binary_without_cipher_compress_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "cipher_compress": false
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}            

hetero_secureboost_testsuite.json

{
    "data": [
        {
            "file": "examples/data/breast_hetero_guest.csv",
            "head": 1,
            "partition": 4,
            "table_name": "breast_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/breast_hetero_host.csv",
            "head": 1,
            "partition": 4,
            "table_name": "breast_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        },
        {
            "file": "examples/data/vehicle_scale_hetero_guest.csv",
            "head": 1,
            "partition": 4,
            "table_name": "vehicle_scale_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/vehicle_scale_hetero_host.csv",
            "head": 1,
            "partition": 4,
            "table_name": "vehicle_scale_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        },
        {
            "file": "examples/data/student_hetero_guest.csv",
            "head": 1,
            "partition": 4,
            "table_name": "student_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/student_hetero_host.csv",
            "head": 1,
            "partition": 4,
            "table_name": "student_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        },
        {
            "file": "examples/data/ionosphere_scale_hetero_guest.csv",
            "head": 1,
            "partition": 4,
            "table_name": "ionosphere_scale_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/ionosphere_scale_hetero_host.csv",
            "head": 1,
            "partition": 4,
            "table_name": "ionosphere_scale_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        },
        {
            "file": "examples/data/motor_hetero_guest.csv",
            "head": 1,
            "partition": 4,
            "table_name": "motor_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/motor_hetero_host_1.csv",
            "head": 1,
            "partition": 4,
            "table_name": "motor_hetero_host_1",
            "namespace": "experiment",
            "role": "host_0"
        },
        {
            "file": "examples/data/motor_hetero_host_2.csv",
            "head": 1,
            "partition": 4,
            "table_name": "motor_hetero_host_2",
            "namespace": "experiment",
            "role": "host_1"
        }
    ],
    "tasks": {
        "train_binary": {
            "conf": "./test_secureboost_train_binary_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "warm_start_train": {
            "conf": "./test_secureboost_warm_start_conf.json",
            "dsl": "./test_secureboost_warm_start_dsl.json"
        },
        "train_complete_secure": {
            "conf": "./test_secureboost_train_complete_secure_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "train_binary_without_cipher_compress": {
            "conf": "./test_secureboost_train_binary_without_cipher_compress_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "train_binary_predict": {
            "conf": "./test_predict_conf.json",
            "dsl": "./test_predict_dsl.json",
            "deps": "train_binary"
        },
        "train_binary_EINI_predict": {
            "conf": "./test_EINI_predict_conf.json",
            "dsl": "./test_predict_dsl.json",
            "deps": "train_binary"
        },
        "train_multi": {
            "conf": "./test_secureboost_train_multi_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "train_regression": {
            "conf": "./test_secureboost_train_regression_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "cv_binary": {
            "conf": "./test_secureboost_cross_validation_binary_conf.json",
            "dsl": "./test_secureboost_cross_validation_dsl.json"
        },
        "cv_multi": {
            "conf": "./test_secureboost_cross_validation_multi_conf.json",
            "dsl": "./test_secureboost_cross_validation_dsl.json"
        },
        "cv_regression": {
            "conf": "./test_secureboost_cross_validation_regression_conf.json",
            "dsl": "./test_secureboost_cross_validation_dsl.json"
        },
        "train_binary_with_missing_value": {
            "conf": "./test_secureboost_train_binary_with_missing_value_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "train_multi_host": {
            "conf": "./test_secureboost_train_regression_multi_host_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        },
        "train_with_early_stopping": {
            "conf": "./test_secureboost_train_with_early_stopping_conf.json",
            "dsl": "./test_secureboost_train_dsl.json"
        }
    }
}            

test_predict_dsl.json

{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "intersection_0": {
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "module": "Intersection",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                },
                "model": [
                    "pipeline.data_transform_0.model"
                ]
            },
            "module": "DataTransform",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_secure_boost_0": {
            "input": {
                "data": {
                    "test_data": [
                        "intersection_0.data"
                    ]
                },
                "model": [
                    "pipeline.hetero_secure_boost_0.model"
                ]
            },
            "module": "HeteroSecureBoost",
            "output": {
                "data": [
                    "data"
                ]
            }
        }
    }
}            

test_secureboost_cross_validation_multi_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "cv_param": {
                    "need_cv": true,
                    "n_splits": 5,
                    "shuffle": false,
                    "random_seed": 103
                },
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    }
                }
            },
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            }
        }
    }
}            

test_secureboost_train_mix_mode_binary_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "boosting_strategy": "mix"
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}

test_secureboost_train_layered_mode_multi_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "boosting_strategy": "layered"
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}

test_secureboost_train_mix_mode_multi_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                },
                "boosting_strategy": "mix"
            },
            "evaluation_0": {
                "eval_type": "multi"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}

test_secureboost_train_binary_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            },
            "evaluation_0": {
                "eval_type": "binary"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            },
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    }
                }
            }
        }
    }
}            

test_secureboost_train_regression_multi_host_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998,
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "regression",
                "objective_param": {
                    "objective": "lse"
                },
                "num_trees": 3,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3
                }
            },
            "evaluation_0": {
                "eval_type": "regression"
            }
        },
        "role": {
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true,
                        "label_name": "motor_speed",
                        "label_type": "float",
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "label_name": "motor_speed",
                        "label_type": "float",
                        "output_format": "dense"
                    },
                    "reader_1": {
                        "table": {
                            "name": "motor_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "motor_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "1": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_1": {
                        "table": {
                            "name": "motor_hetero_host_2",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "motor_hetero_host_2",
                            "namespace": "experiment"
                        }
                    }
                },
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_1": {
                        "table": {
                            "name": "motor_hetero_host_1",
                            "namespace": "experiment"
                        }
                    },
                    "reader_0": {
                        "table": {
                            "name": "motor_hetero_host_1",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

test_secureboost_train_multi_with_mo_conf.json

{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            9998
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_secure_boost_0": {
                "task_type": "classification",
                "objective_param": {
                    "objective": "cross_entropy"
                },
                "num_trees": 5,
                "validation_freqs": 1,
                "encrypt_param": {
                    "method": "Paillier"
                },
                "tree_param": {
                    "max_depth": 3,
                    "criterion_params": [0.5, 0]
                },
                "multi_mode": "multi_output"
            },
            "evaluation_0": {
                "eval_type": "multi"
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    },
                    "data_transform_1": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "guest": {
                "0": {
                    "reader_1": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "data_transform_1": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "vehicle_scale_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}            

Hetero Fast SecureBoost

We support Hetero Fast SecureBoost, in abbreviation, fast SBT, in FATE-1.5. The fast SBT uses guest features and host features alternately to build trees, in order to save encryption costs and communication costs. In fast SBT, we support MIX mode and LAYERED mode and they use different strategies while building decision trees.

MIX mode

In mix mode, we offer a new parameter 'tree_num_per_party'. Every participated party will build 'tree_num_per_party' trees using their local features, and this procedure will be repeated until reach the max tree number. Figure 5 illustrates the mix mode.

While building a guest tree, the guest party side simply computes g/h and finds the best split points, other host parties will skip this tree and wait. While building a host tree, the related host party will receive encrypted g/h and find the best split points with the assistant of the guest party. The structures of host trees and split points will be preserved on the host side while leaf weights will be preserved on the guest side. In this way, encryption and communication costs are reduced by half. (If there are two parties)

While conducting inference, every party will traverse its trees locally. All hosts will send the final leaf id to guests and the guest retrieves leaf weights using received leaf id. The prediction only needs one communication in mix mode.

LAYERED mode

In layered mode, only supports one guest party and one host party. The host will be responsible for building the first "host_depth" layers, with the help of the guest, and the guest will be responsible for the next "guest_depth" layers. All trees will be built in this 'layered' manner.

The benefits of layered mod is obvious, like the mix mode, parts of communication costs and encryption costs will be saved in the process of training. When predicting, we only need one communication because all host can conduct inferences of host layers locally.

According to experiments on our standard data sets, mix mode and layered mode of Fast SBT can still give performances (sometimes even better) equivalent to standard Hetero SecureBoost, even the training data is unbalanced distributed in different parties or contains noise features. (Binary, multi-class, and regression tasks are tested). At the same time, the time consumption of FAST SBT is reduced by 30% ~ 50% on average.

Optimization in learning

Fast SBT uses guest features and host features alternately by trees/layers to reduce encryption and communication costs.

• Prediction only needs one communication round.

Applications

Fast SBT supports the following applications.

• binary classification, the objective function is sigmoid cross-entropy
• multi classification, the objective function is softmax cross-entropy
• regression, objective function includes least-squared-error-loss、least-absolutely-error-loss、huber-loss、tweedie-loss、fair-loss、 log-cosh-loss

Other features

• In mix mode, every host parties only keep their own tree models. Guest will only keep guest trees and host leaves.
• In mix mode, host side support feature importance calculation (split type is supported, gain type is not supported)
• In layered mode, model exporting setting is the same as the normal-SBT.
• The time consumption of FAST SBT is reduced by 30% ~ 50% on average.

Hetero SecureBoost with Multi-Output(SBT-MO)

In the traditional GBDT setting, the strategy of multi-classification learning is to separate the gradient/hessian of each class and learn a tree for each class independently. Each tree is responsible for predicting a single variable. In the vertical federated scenario, using a traditional single-output-tree-based multi-classification strategy has its limitations: All the computation costs and communication overheads are amplified by the times of the number of labels. It will be extremely time-consuming if we are training on a dataset with many types of labels.

To address the efficiency problem, in FATE-1.8, we propose a novel multi-output-tree based vertical boosting tree techniques for multi-classification tasks. Leaves of multi-output-tree give multi-dimension output, corresponding to every class. Instead of learning trees for every class separately, now we only need to fit one tree at every boosting epoch. We also combines our cipher optimization techniques with SBT-MO. According to our preliminary experiments, on the conditions of reaching the same accuracy, SBT-MO reduces tree building time(ignoring data preprocessing and evaluation time) by over 50%+. For more details of SBT-MO, please refer to SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning.

Optimization in learning

SBT-MO solve multi-classification tasks with multi-output decision trees. Save time when training on a dataset with many types of labels.

Applications

• multi classification, the objective function is softmax cross-entropy

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最后更新: 2022-01-19