federatedml

Federated Machine Learning

[中文]

FederatedML 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, TransferLearning, which support Heterogeneous and Homogeneous styles.
4. Model Evaluation: Binary|Multiclass|Regression Evaluation, Local vs Federated Comparison.
5. Secure Protocol: Provides multiple security protocols for secure multi-party computing and interaction between participants.

Alogorithm List

Algorithm	Module Name	Description	Data Input	Data Output	Model Input	Model Output
DataIO	DataIO	This component is typically the first component of a modeling task. It will transform user- uploaded date into Instance object which can be used for the following components.	DTable, values are raw data.	Transformed DTable, values are data instance define in f ederatedml/ feature/ins tance.py
Intersect	Intersection	Compute intersect data set of two parties without leakage of difference set information. Mainly used in hetero scenario task.	DTable	DTable which keys are occurred in both parties.
Federated Sampling	FederatedSample	Federated Sampling data so that its distribution become balance in each party.This module support both federated and standalone version.	DTable	the sampled data, supports both random and stratified sampling.
Feature Scale	FeatureScale	Module for feature scaling and standardization.	DTable, whose values are instances.	Transformed DTable.		Transform factors like min/max, mean/std.
Hetero Feature Binning	HeteroFeatureBinning	With binning input data, calculates each column's iv and woe and transform data according to the binned information.	DTable with y in guest and without y in host.	Transformed DTable.		iv/woe, split points, event counts, non- event counts etc. of each column.
OneHot Encoder	OneHotEncoder	Transfer a column into one-hot format.	Input DTable.	Transformed DTable with new headers.		Original header and feature values to new header map.
Hetero Feature Selection	HeteroFeatureSelection	Provide 5 types of filters. Each filters can select columns according to user config.	Input DTable.	Transformed DTable with new headers and filtered data instance.	If iv filters used, heter o_binning model is needed.	Whether left or not for each column.
Union	Union	Combine multiple data tables into one.	Input DTable(s).	one DTable with combined values from input DTables.
Hetero-LR	HeteroLR	Build hetero logistic regression module through multiple parties.	Input DTable.			Logistic Regression model.
Local Baseline	LocalBaseline	Wrapper that runs sklearn Logistic Regression model with local data.	Input DTable.			Logistic Regression. model.
Hetero-LinR	HeteroLinR	Build hetero linear regression module through multiple parties.	Input DTable.			Linear Regression model.
Hetero-Poisson	HeteroPoisson	Build hetero poisson regression module through multiple parties.	Input DTable.			Poisson Regression model.
Homo-LR	HomoLR	Build homo logistic regression module through multiple parties.	Input DTable.			Logistic Regression model.
Homo-NN	HomoNN	Build homo neural network module through multiple parties.	Input Dtable.			Neural Network model.
Hetero Secure Boosting	HeteroSecureBoost	Build hetero secure boosting module through multiple parties.	DTable, values are instances.			SecureBoost Model, consists of model-meta and model- param
Evaluation	Evaluation
Hetero Pearson	HeteroPearson
Hetero-NN	HeteroNN	Build hetero neural network module.	Input Dtable.			Model Output: heero neural network model.
Homo Secure Boosting	HomoSecureBoost	Build homo secure boosting module through multiple parties.	DTable, values are instances.			SecureBoost Model, consists of model-meta and model- param

.. toctree::
   :maxdepth: 2

   util/README
   statistic/intersect/README
   feature/README
   statistic/union/README
   linear_model/logistic_regression/README
   local_baseline/README
   linear_model/linear_regression/README
   linear_model/poisson_regression/README
   nn/homo_nn/README
   tree/README
   evaluation/README
   statistic/correlation/README
   nn/hetero_nn/README
   model_selection/stepwise/README

Secure Protocol

.. toctree::
   :maxdepth: 2

   secureprotol/README