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Federated Linear Regression

Linear Regression(LinR) is a simple statistic model widely used for predicting continuous numbers. FATE provides Heterogeneous Linear Regression(HeteroLinR). HeteroLinR also supports multi-Host training. You can specify multiple hosts in the job configuration file like the provided examples/dsl/v2/hetero_linear_regression.

Here we simplify participants of the federation process into three parties. Party A represents Guest, party B represents Host. Party C, which is also known as “Arbiter,” is a third party that works as coordinator. Party C is responsible for generating private and public keys.

Heterogeneous LinR

The process of HeteroLinR training is shown below:

Figure 1 (Federated HeteroLinR
Principle)

A sample alignment process is conducted before training. The sample alignment process identifies overlapping samples in databases of all parties. The federated model is built based on the overlapping samples. The whole sample alignment process is conducted in encryption mode, and so confidential information (e.g. sample ids) will not be leaked.

In the training process, party A and party B each compute the elements needed for final gradients. Arbiter aggregates, calculates, and transfers back the final gradients to corresponding parties. For more details on the secure model-building process, please refer to this paper.

Param

linear_regression_param

Classes

LinearParam (BaseParam)

Parameters used for Linear Regression.

Parameters:

Name Type Description Default
penalty {'L2' or 'L1'}

Penalty method used in LinR. Please note that, when using encrypted version in HeteroLinR, 'L1' is not supported.

'L2'
tol float, default: 1e-4

The tolerance of convergence

0.0001
alpha float, default: 1.0

Regularization strength coefficient.

1.0
optimizer {'sgd', 'rmsprop', 'adam', 'sqn', 'adagrad'}

Optimize method

'sgd'
batch_size int, default: -1

Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.

-1
learning_rate float, default: 0.01

Learning rate

0.01
max_iter int, default: 20

The maximum iteration for training.

20
init_param InitParam object, default: default InitParam object

Init param method object.

<federatedml.param.init_model_param.InitParam object at 0x7f27551d5310>
early_stop {'diff', 'abs', 'weight_dff'}

Method used to judge convergence. a) diff: Use difference of loss between two iterations to judge whether converge. b) abs: Use the absolute value of loss to judge whether converge. i.e. if loss < tol, it is converged. c) weight_diff: Use difference between weights of two consecutive iterations

'diff'
predict_param PredictParam object, default: default PredictParam object

predict param

<federatedml.param.predict_param.PredictParam object at 0x7f27551d5a50>
encrypt_param EncryptParam object, default: default EncryptParam object

encrypt param

<federatedml.param.encrypt_param.EncryptParam object at 0x7f27551d5ed0>
encrypted_mode_calculator_param EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object

encrypted mode calculator param

<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27551d5fd0>
cv_param CrossValidationParam object, default: default CrossValidationParam object

cv param

<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27551d5f10>
decay int or float, default: 1

Decay rate for learning rate. learning rate will follow the following decay schedule. lr = lr0/(1+decay*t) if decay_sqrt is False. If decay_sqrt is True, lr = lr0 / sqrt(1+decay*t) where t is the iter number.

1
decay_sqrt Bool, default: True

lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t)

True
validation_freqs int, list, tuple, set, or None

validation frequency during training, required when using early stopping. 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 "max_iter" is recommended, otherwise, you will miss the validation scores of the last training iteration.

None
early_stopping_rounds int, default: None

If positive number specified, at every specified training rounds, program checks for early stopping criteria. Validation_freqs must also be set when using early stopping.

None
metrics list or None, default: None

Specify which metrics to be used when performing evaluation during training process. If metrics have not improved at early_stopping rounds, trianing stops before convergence. If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']

None
use_first_metric_only bool, default: False

Indicate whether to use the first metric in metrics as the only criterion for early stopping judgement.

False
floating_point_precision None or integer

if not None, use floating_point_precision-bit to speed up calculation, e.g.: convert an x to round(x * 2**floating_point_precision) during Paillier operation, divide the result by 2**floating_point_precision in the end.

23
callback_param CallbackParam object

callback param

<federatedml.param.callback_param.CallbackParam object at 0x7f27551d5f50>
Source code in federatedml/param/linear_regression_param.py
class LinearParam(BaseParam):
    """
    Parameters used for Linear Regression.

    Parameters
    ----------
    penalty : {'L2' or 'L1'}
        Penalty method used in LinR. Please note that, when using encrypted version in HeteroLinR,
        'L1' is not supported.

    tol : float, default: 1e-4
        The tolerance of convergence

    alpha : float, default: 1.0
        Regularization strength coefficient.

    optimizer : {'sgd', 'rmsprop', 'adam', 'sqn', 'adagrad'}
        Optimize method

    batch_size : int, default: -1
        Batch size when updating model. -1 means use all data in a batch. i.e. Not to use mini-batch strategy.

    learning_rate : float, default: 0.01
        Learning rate

    max_iter : int, default: 20
        The maximum iteration for training.

    init_param: InitParam object, default: default InitParam object
        Init param method object.

    early_stop : {'diff', 'abs', 'weight_dff'}
        Method used to judge convergence.
            a)	diff: Use difference of loss between two iterations to judge whether converge.
            b)	abs: Use the absolute value of loss to judge whether converge. i.e. if loss < tol, it is converged.
            c)  weight_diff: Use difference between weights of two consecutive iterations

    predict_param: PredictParam object, default: default PredictParam object
        predict param

    encrypt_param: EncryptParam object, default: default EncryptParam object
        encrypt param

    encrypted_mode_calculator_param: EncryptedModeCalculatorParam object, default: default EncryptedModeCalculatorParam object
        encrypted mode calculator param

    cv_param: CrossValidationParam object, default: default CrossValidationParam object
        cv param

    decay: int or float, default: 1
        Decay rate for learning rate. learning rate will follow the following decay schedule.
        lr = lr0/(1+decay*t) if decay_sqrt is False. If decay_sqrt is True, lr = lr0 / sqrt(1+decay*t)
        where t is the iter number.

    decay_sqrt: Bool, default: True
        lr = lr0/(1+decay*t) if decay_sqrt is False, otherwise, lr = lr0 / sqrt(1+decay*t)

    validation_freqs: int, list, tuple, set, or None
        validation frequency during training, required when using early stopping.
        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 "max_iter" is recommended, otherwise, you will miss the validation scores of the last training iteration.

    early_stopping_rounds: int, default: None
        If positive number specified, at every specified training rounds, program checks for early stopping criteria.
        Validation_freqs must also be set when using early stopping.

    metrics: list or None, default: None
        Specify which metrics to be used when performing evaluation during training process. If metrics have not improved at early_stopping rounds, trianing stops before convergence.
        If set as empty, default metrics will be used. For regression tasks, default metrics are ['root_mean_squared_error', 'mean_absolute_error']

    use_first_metric_only: bool, default: False
        Indicate whether to use the first metric in `metrics` as the only criterion for early stopping judgement.

    floating_point_precision: None or integer
        if not None, use floating_point_precision-bit to speed up calculation,
        e.g.: convert an x to round(x * 2**floating_point_precision) during Paillier operation, divide
                the result by 2**floating_point_precision in the end.
    callback_param: CallbackParam object
        callback param

    """

    def __init__(self, penalty='L2',
                 tol=1e-4, alpha=1.0, optimizer='sgd',
                 batch_size=-1, learning_rate=0.01, init_param=InitParam(),
                 max_iter=20, early_stop='diff', predict_param=PredictParam(),
                 encrypt_param=EncryptParam(), sqn_param=StochasticQuasiNewtonParam(),
                 encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
                 cv_param=CrossValidationParam(), decay=1, decay_sqrt=True, validation_freqs=None,
                 early_stopping_rounds=None, stepwise_param=StepwiseParam(), metrics=None, use_first_metric_only=False,
                 floating_point_precision=23, callback_param=CallbackParam()):
        super(LinearParam, self).__init__()
        self.penalty = penalty
        self.tol = tol
        self.alpha = alpha
        self.optimizer = optimizer
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.init_param = copy.deepcopy(init_param)
        self.max_iter = max_iter
        self.early_stop = early_stop
        self.encrypt_param = encrypt_param
        self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
        self.cv_param = copy.deepcopy(cv_param)
        self.predict_param = copy.deepcopy(predict_param)
        self.decay = decay
        self.decay_sqrt = decay_sqrt
        self.validation_freqs = validation_freqs
        self.sqn_param = copy.deepcopy(sqn_param)
        self.early_stopping_rounds = early_stopping_rounds
        self.stepwise_param = copy.deepcopy(stepwise_param)
        self.metrics = metrics or []
        self.use_first_metric_only = use_first_metric_only
        self.floating_point_precision = floating_point_precision
        self.callback_param = copy.deepcopy(callback_param)

    def check(self):
        descr = "linear_regression_param's "

        if self.penalty is None:
            self.penalty = 'NONE'
        elif type(self.penalty).__name__ != "str":
            raise ValueError(
                descr + "penalty {} not supported, should be str type".format(self.penalty))

        self.penalty = self.penalty.upper()
        if self.penalty not in ['L1', 'L2', 'NONE']:
            raise ValueError(
                "penalty {} not supported, penalty should be 'L1', 'L2' or 'none'".format(self.penalty))

        if type(self.tol).__name__ not in ["int", "float"]:
            raise ValueError(
                descr + "tol {} not supported, should be float type".format(self.tol))

        if type(self.alpha).__name__ not in ["int", "float"]:
            raise ValueError(
                descr + "alpha {} not supported, should be float type".format(self.alpha))

        if type(self.optimizer).__name__ != "str":
            raise ValueError(
                descr + "optimizer {} not supported, should be str type".format(self.optimizer))
        else:
            self.optimizer = self.optimizer.lower()
            if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'sqn']:
                raise ValueError(
                    descr + "optimizer not supported, optimizer should be"
                    " 'sgd', 'rmsprop', 'adam', 'sqn' or 'adagrad'")

        if type(self.batch_size).__name__ not in ["int", "long"]:
            raise ValueError(
                descr + "batch_size {} not supported, should be int type".format(self.batch_size))
        if self.batch_size != -1:
            if type(self.batch_size).__name__ not in ["int", "long"] \
                    or self.batch_size < consts.MIN_BATCH_SIZE:
                raise ValueError(descr + " {} not supported, should be larger than {} or "
                                         "-1 represent for all data".format(self.batch_size, consts.MIN_BATCH_SIZE))

        if type(self.learning_rate).__name__ not in ["int", "float"]:
            raise ValueError(
                descr + "learning_rate {} not supported, should be float type".format(
                    self.learning_rate))

        self.init_param.check()

        if type(self.max_iter).__name__ != "int":
            raise ValueError(
                descr + "max_iter {} not supported, should be int type".format(self.max_iter))
        elif self.max_iter <= 0:
            raise ValueError(
                descr + "max_iter must be greater or equal to 1")

        if type(self.early_stop).__name__ != "str":
            raise ValueError(
                descr + "early_stop {} not supported, should be str type".format(
                    self.early_stop))
        else:
            self.early_stop = self.early_stop.lower()
            if self.early_stop not in ['diff', 'abs', 'weight_diff']:
                raise ValueError(
                    descr + "early_stop not supported, early_stop should be 'weight_diff', 'diff' or 'abs'")

        self.encrypt_param.check()
        if self.encrypt_param.method != consts.PAILLIER:
            raise ValueError(
                descr + "encrypt method supports 'Paillier' only")

        self.encrypted_mode_calculator_param.check()

        if type(self.decay).__name__ not in ["int", "float"]:
            raise ValueError(
                descr + "decay {} not supported, should be 'int' or 'float'".format(self.decay)
            )
        if type(self.decay_sqrt).__name__ not in ["bool"]:
            raise ValueError(
                descr + "decay_sqrt {} not supported, should be 'bool'".format(self.decay)
            )
        self.sqn_param.check()
        self.stepwise_param.check()

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

        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.floating_point_precision is not None and \
                (not isinstance(self.floating_point_precision, int) or
                 self.floating_point_precision < 0 or self.floating_point_precision > 64):
            raise ValueError("floating point precision should be null or a integer between 0 and 64")
        self.callback_param.check()
        return True
__init__(self, penalty='L2', tol=0.0001, alpha=1.0, optimizer='sgd', batch_size=-1, learning_rate=0.01, init_param=<federatedml.param.init_model_param.InitParam object at 0x7f27551d5310>, max_iter=20, early_stop='diff', predict_param=<federatedml.param.predict_param.PredictParam object at 0x7f27551d5a50>, encrypt_param=<federatedml.param.encrypt_param.EncryptParam object at 0x7f27551d5ed0>, sqn_param=<federatedml.param.sqn_param.StochasticQuasiNewtonParam object at 0x7f27551d5790>, encrypted_mode_calculator_param=<federatedml.param.encrypted_mode_calculation_param.EncryptedModeCalculatorParam object at 0x7f27551d5fd0>, cv_param=<federatedml.param.cross_validation_param.CrossValidationParam object at 0x7f27551d5f10>, decay=1, decay_sqrt=True, validation_freqs=None, early_stopping_rounds=None, stepwise_param=<federatedml.param.stepwise_param.StepwiseParam object at 0x7f27551d5a10>, metrics=None, use_first_metric_only=False, floating_point_precision=23, callback_param=<federatedml.param.callback_param.CallbackParam object at 0x7f27551d5f50>) special
Source code in federatedml/param/linear_regression_param.py
def __init__(self, penalty='L2',
             tol=1e-4, alpha=1.0, optimizer='sgd',
             batch_size=-1, learning_rate=0.01, init_param=InitParam(),
             max_iter=20, early_stop='diff', predict_param=PredictParam(),
             encrypt_param=EncryptParam(), sqn_param=StochasticQuasiNewtonParam(),
             encrypted_mode_calculator_param=EncryptedModeCalculatorParam(),
             cv_param=CrossValidationParam(), decay=1, decay_sqrt=True, validation_freqs=None,
             early_stopping_rounds=None, stepwise_param=StepwiseParam(), metrics=None, use_first_metric_only=False,
             floating_point_precision=23, callback_param=CallbackParam()):
    super(LinearParam, self).__init__()
    self.penalty = penalty
    self.tol = tol
    self.alpha = alpha
    self.optimizer = optimizer
    self.batch_size = batch_size
    self.learning_rate = learning_rate
    self.init_param = copy.deepcopy(init_param)
    self.max_iter = max_iter
    self.early_stop = early_stop
    self.encrypt_param = encrypt_param
    self.encrypted_mode_calculator_param = copy.deepcopy(encrypted_mode_calculator_param)
    self.cv_param = copy.deepcopy(cv_param)
    self.predict_param = copy.deepcopy(predict_param)
    self.decay = decay
    self.decay_sqrt = decay_sqrt
    self.validation_freqs = validation_freqs
    self.sqn_param = copy.deepcopy(sqn_param)
    self.early_stopping_rounds = early_stopping_rounds
    self.stepwise_param = copy.deepcopy(stepwise_param)
    self.metrics = metrics or []
    self.use_first_metric_only = use_first_metric_only
    self.floating_point_precision = floating_point_precision
    self.callback_param = copy.deepcopy(callback_param)
check(self)
Source code in federatedml/param/linear_regression_param.py
def check(self):
    descr = "linear_regression_param's "

    if self.penalty is None:
        self.penalty = 'NONE'
    elif type(self.penalty).__name__ != "str":
        raise ValueError(
            descr + "penalty {} not supported, should be str type".format(self.penalty))

    self.penalty = self.penalty.upper()
    if self.penalty not in ['L1', 'L2', 'NONE']:
        raise ValueError(
            "penalty {} not supported, penalty should be 'L1', 'L2' or 'none'".format(self.penalty))

    if type(self.tol).__name__ not in ["int", "float"]:
        raise ValueError(
            descr + "tol {} not supported, should be float type".format(self.tol))

    if type(self.alpha).__name__ not in ["int", "float"]:
        raise ValueError(
            descr + "alpha {} not supported, should be float type".format(self.alpha))

    if type(self.optimizer).__name__ != "str":
        raise ValueError(
            descr + "optimizer {} not supported, should be str type".format(self.optimizer))
    else:
        self.optimizer = self.optimizer.lower()
        if self.optimizer not in ['sgd', 'rmsprop', 'adam', 'adagrad', 'sqn']:
            raise ValueError(
                descr + "optimizer not supported, optimizer should be"
                " 'sgd', 'rmsprop', 'adam', 'sqn' or 'adagrad'")

    if type(self.batch_size).__name__ not in ["int", "long"]:
        raise ValueError(
            descr + "batch_size {} not supported, should be int type".format(self.batch_size))
    if self.batch_size != -1:
        if type(self.batch_size).__name__ not in ["int", "long"] \
                or self.batch_size < consts.MIN_BATCH_SIZE:
            raise ValueError(descr + " {} not supported, should be larger than {} or "
                                     "-1 represent for all data".format(self.batch_size, consts.MIN_BATCH_SIZE))

    if type(self.learning_rate).__name__ not in ["int", "float"]:
        raise ValueError(
            descr + "learning_rate {} not supported, should be float type".format(
                self.learning_rate))

    self.init_param.check()

    if type(self.max_iter).__name__ != "int":
        raise ValueError(
            descr + "max_iter {} not supported, should be int type".format(self.max_iter))
    elif self.max_iter <= 0:
        raise ValueError(
            descr + "max_iter must be greater or equal to 1")

    if type(self.early_stop).__name__ != "str":
        raise ValueError(
            descr + "early_stop {} not supported, should be str type".format(
                self.early_stop))
    else:
        self.early_stop = self.early_stop.lower()
        if self.early_stop not in ['diff', 'abs', 'weight_diff']:
            raise ValueError(
                descr + "early_stop not supported, early_stop should be 'weight_diff', 'diff' or 'abs'")

    self.encrypt_param.check()
    if self.encrypt_param.method != consts.PAILLIER:
        raise ValueError(
            descr + "encrypt method supports 'Paillier' only")

    self.encrypted_mode_calculator_param.check()

    if type(self.decay).__name__ not in ["int", "float"]:
        raise ValueError(
            descr + "decay {} not supported, should be 'int' or 'float'".format(self.decay)
        )
    if type(self.decay_sqrt).__name__ not in ["bool"]:
        raise ValueError(
            descr + "decay_sqrt {} not supported, should be 'bool'".format(self.decay)
        )
    self.sqn_param.check()
    self.stepwise_param.check()

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

    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.floating_point_precision is not None and \
            (not isinstance(self.floating_point_precision, int) or
             self.floating_point_precision < 0 or self.floating_point_precision > 64):
        raise ValueError("floating point precision should be null or a integer between 0 and 64")
    self.callback_param.check()
    return True

Features

  1. L1 & L2 regularization

  2. Mini-batch mechanism

  3. Five optimization methods:

    • sgd
      gradient descent with arbitrary batch size

    • rmsprop
      RMSProp

    • adam
      Adam

    • adagrad
      AdaGrad

    • nesterov_momentum_sgd
      Nesterov Momentum

    • stochastic quansi-newton
      The algorithm details can refer to this paper.

  4. Three converge criteria:

    • diff
      Use difference of loss between two iterations, not available for multi-host training

    • abs
      Use the absolute value of loss

    • weight_diff
      Use difference of model weights

  5. Support multi-host modeling task. For details on how to configure for multi-host modeling task, please refer to this guide

  6. Support validation for every arbitrary iterations

  7. Learning rate decay mechanism

  8. Support early stopping mechanism, which checks for performance change on specified metrics over training rounds. Early stopping is triggered when no improvement is found at early stopping rounds.

  9. Support sparse format data as input.

  10. Support stepwise. For details on stepwise mode, please refer to stepwise .


Last update: 2021-11-19
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