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Federated Machine Learning

[中文]

FATE-ML includes implementation of many common machine learning algorithms on federated learning. All modules are developed in a decoupling modular approach to enhance scalability. Specifically, we provide:

  1. Federated Statistic: PSI, Union, Pearson Correlation, etc.
  2. Federated Feature Engineering: Feature Sampling, Feature Binning, Feature Selection, etc.
  3. Federated Machine Learning Algorithms: LR, GBDT, DNN
  4. Model Evaluation: Binary | Multiclass | Regression evaluation
  5. Secure Protocol: Provides multiple security protocols for secure multi-party computing and interaction between participants.

Algorithm List

For tutorial on running modules directly(without FATE-Client) with launcher, please refer here.

Algorithm Module Name Examples Description Data Input Data Output Model Input Model Output
Reader Component to passing namespace,name to downstream tasks output_data
PSI PSI psi Compute intersect data set of multiple parties without leakage of difference set information. Mainly used in hetero scenario task. input_data output_data
Sampling Sample sample Federated Sampling data so that its distribution become balance in each party.This module supports local and federation scenario. input_data output_data
Data Split DataSplit data split Split one data table into 3 tables by given ratio or count, this module supports local and federation scenario input_data train_output_data, validate_output_data, test_output_data
Feature Scale FeatureScale feature scale module for feature scaling and standardization. train_data, test_data train_output_data, test_output_data input_model output_model
Data Statistics Statistics statistics This component will do some statistical work on the data, including statistical mean, maximum and minimum, median, etc. input_data output_model
Hetero Feature Binning HeteroFeatureBinning hetero feature binning With binning input data, calculates each column's iv and woe and transform data according to the binned information. train_data, test_data train_output_data, test_output_data input_model output_model
Hetero Feature Selection HeteroFeatureSelection hetero feature selection Provide 3 types of filters. Each filters can select columns according to user config train_data, test_data train_output_data, test_output_data input_models, input_model output_model
Coordinated-LR CoordinatedLR coordinated LR Build hetero logistic regression model through multiple parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model
Coordinated-LinR CoordinatedLinR coordinated LinR Build hetero linear regression model through multiple parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model
Homo-LR HomoLR homo lr Build homo logistic regression model through multiple parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model
Homo-NN HomoNN homo nn Build homo neural network model through multiple parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model
Hetero-NN HeteroNN hetero nn Build hetero neural network model through multiple parties. train_data, validate_data, test_data train_output_data, test_output_data warm_start_model, input_model output_model
Hetero Secure Boosting HeteroSecureBoost hetero secureboost Build hetero secure boosting model through multiple parties train_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas warm_start_model, input_model output_model
Evaluation Evaluation evaluation Output the model evaluation metrics for user. input_datas
Union Union union Combine multiple data tables into one. input_datas output_data
SSHE-LR SSHELR SSHE LR Build hetero logistic regression model through two parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model
SSHE-LinR SSHELinR SSHE LinR Build hetero linear regression model through two parties. train_data, validate_data, test_data, cv_data train_output_data, test_output_data, cv_output_datas input_model, warm_start_model output_model