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federatedml

README.md

Federated Machine Learning

Federatedml includes implementation of many common machine learning algorithms as well as necessary utility tools. All modules are developed in a decoupling modular approach to enhance scalability. Specifically, we provide:

  1. FML Algorithms: Federated machine learning algorithms serving for DataIO, Data-preprocessing, feature engineering and modeling. More details are listed below.

  2. Utilities: Tools that enable federated learning such as encryption tools, statistic modules, parameter definitions, and transfer variable autogenerator etc.

  3. Framework: Kits and base models for developing new algorithm modules. Framework provides reusable functions to standardize modules and make them compact.

  4. Secure Protocol: Provides multiple security protocols for more secure multi-party interaction calculations.

samples
Figure 1： Federated HeteroLR Principle

Algorithm List

1. 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.

  • Corresponding module name: DataIO

  • Data Input: DTable, values are raw data.

  • Data Output: Transformed DTable, values are data instance define in federatedml/feature/instance.py

2. Intersect

Compute intersect data set of two parties without leakage of difference set information. Mainly used in hetero scenario task.

  • Corresponding module name: Intersection

  • Data Input: DTable

  • Data Output: DTable which keys are occurred in both parties.

3. Federated Sampling

Federated Sampling data so that its distribution become balance in each party.This module support both federated and standalone version

  • Corresponding module name: FederatedSample

  • Data Input: DTable

  • Data Output: the sampled data, supports both random and stratified sampling.

4. Feature Scale

Module for feature scaling and standardization.

  • Corresponding module name: FeatureScale

  • Data Input: DTable, whose values are instances.

  • Data Output: Transformed DTable.

  • Model Output: Transform factors like min/max, mean/std.

5. Hetero Feature Binning

With binning input data, calculates each column's iv and woe and transform data according to the binned information.

  • Corresponding module name: HeteroFeatureBinning

  • Data Input: DTable with y in guest and without y in host.

  • Data Output: Transformed DTable.

  • Model Output: iv/woe, split points, event counts, non-event counts etc. of each column.

6. OneHot Encoder

Transfer a column into one-hot format.

  • Corresponding module name: OneHotEncoder
  • Data Input: Input DTable.
  • Data Output: Transformed DTable with new headers.
  • Model Output: Original header and feature values to new header map.

7. Hetero Feature Selection

Provide 5 types of filters. Each filters can select columns according to user config.

  • Corresponding module name: HeteroFeatureSelection
  • Data Input: Input DTable.
  • Model Input: If iv filters used, hetero_binning model is needed.
  • Data Output: Transformed DTable with new headers and filtered data instance.
  • Model Output: Whether left or not for each column.

8. Hetero-LR

Build hetero logistic regression module through multiple parties.

  • Corresponding module name: HeteroLR
  • Data Input: Input DTable.
  • Model Output: Logistic Regression model.

9. Hetero-LinR

Build hetero linear regression module through multiple parties.

  • Corresponding module name: HeteroLinR
  • Data Input: Input DTable.
  • Model Output: Linear Regression model.

10. Hetero-Poisson

Build hetero poisson regression module through multiple parties.

  • Corresponding module name: HeteroPoisson
  • Data Input: Input DTable.
  • Model Output: Poisson Regression model.

11. Homo-LR

Build homo logistic regression module through multiple parties.

  • Corresponding module name: HomoLR
  • Data Input: Input DTable.
  • Model Output: Logistic Regression model.

12. Homo-NN

Build homo neural network module through multiple parties.

  • Corresponding module name: HomoNN
  • Data Input: Input DTable.
  • Model Output: Neural Network model.

13. Hetero Secure Boosting

Build hetero secure boosting model through multiple parties.

Corresponding module name: HeteroSecureBoost

  • Data Input: DTable, values are instances.
  • Model Output: SecureBoost Model, consists of model-meta and model-param

14. Evaluation

Output the model evaluation metrics for user.

  • Corresponding module name: Evaluation

More available algorithms are coming soon.