README.rst

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 | Clustering Evaluation, Local vs Federated Comparison.
5. Secure Protocol: Provides multiple security protocols for secure multi-party computing and interaction between participants.

Algorithm List

Algorithm

Algorithm	Module Name	Description	Data Input	Data Output	Model Input	Model Output
Reader	Reader	This component loads and transforms data from storage engine so that data is compatible with FATE computing engine	Original Data	Transformed Data
DataIO	DataIO	This component transforms user-uploaded date into Instance object(deprecate in FATe-v1.7, use DataTransform instead).	Table, values are raw data.	Transformed Table, values are data instance defined here		DataIO Model
DataTransform	DataTransform	This component transforms user-uploaded date into Instance object.	Table, values are raw data.	Transformed Table, values are data instance defined here		DataTransform Model
Intersect	Intersection	Compute intersect data set of multiple parties without leakage of difference set information. Mainly used in hetero scenario task.	Table.	Table with only common instance keys.		Intersect Model
Federated Sampling	FederatedSample	Federated Sampling data so that its distribution become balance in each party.This module supports standalone and federated versions.	Table	Table of sampled data; both random and stratified sampling methods are supported.
Feature Scale	FeatureScale	module for feature scaling and standardization.	Table，values are instances.	Transformed Table.	Transform factors like min/max, mean/std.
Hetero Feature Binning	Hetero Feature Binning	With binning input data, calculates each column's iv and woe and transform data according to the binned information.	Table, values are instances.	Transformed Table.		iv/woe, split points, event count, non-event count etc. of each column.
Homo Feature Binning	Homo Feature Binning	Calculate quantile binning through multiple parties	Table	Transformed Table		Split points of each column
OneHot Encoder	OneHotEncoder	Transfer a column into one-hot format.	Table, values are instances.	Transformed Table with new header.		Feature-name mapping between original header and new header.
Hetero Feature Selection	HeteroFeatureSelection	Provide 5 types of filters. Each filters can select columns according to user config	Table	Transformed Table with new header and filtered data instance.	If iv filters used, hetero_binning model is needed.	Whether each column is filtered.
Union	Union	Combine multiple data tables into one.	Tables.	Table with combined values from input Tables.
Hetero-LR	HeteroLR	Build hetero logistic regression model through multiple parties.	Table, values are instances	Table, values are instances.		Logistic Regression Model, consists of model-meta and model-param.
Local Baseline	LocalBaseline	Wrapper that runs sklearn(scikit-learn) Logistic Regression model with local data.	Table, values are instances.	Table, values are instances.
Hetero-LinR	HeteroLinR	Build hetero linear regression model through multiple parties.	Table, values are instances.	Table, values are instances.		Linear Regression Model, consists of model-meta and model-param.
Hetero-Poisson	HeteroPoisson	Build hetero poisson regression model through multiple parties.	Table, values are instances.	Table, values are instances.		Poisson Regression Model, consists of model-meta and model-param.
Homo-LR	HomoLR	Build homo logistic regression model through multiple parties.	Table, values are instances.	Table, values are instances.		Logistic Regression Model, consists of model-meta and model-param.
Homo-NN	HomoNN	Build homo neural network model through multiple parties.	Table, values are instances.	Table, values are instances.		Neural Network Model, consists of model-meta and model-param.
Hetero Secure Boosting	HeteroSecureBoost	Build hetero secure boosting model through multiple parties	Table, values are instances.	Table, values are instances.		SecureBoost Model, consists of model-meta and model-param.
Hetero Fast Secure Boosting	HeteroFastSecureBoost	Build hetero secure boosting model through multiple parties in layered/mix manners.	Table, values are instances.	Table, values are instances.		FastSecureBoost Model, consists of model-meta and model-param.
Hetero Secure Boost Feature Transformer	SBT Feature Transformer	This component can encode sample using Hetero SBT leaf indices.	Table, values are instances.	Table, values are instances.		SBT Transformer Model
Evaluation	Evaluation	Output the model evaluation metrics for user.	Table(s), values are instances.
Hetero Pearson	HeteroPearson	Calculate hetero correlation of features from different parties.	Table, values are instances.
Hetero-NN	HeteroNN	Build hetero neural network model.	Table, values are instances.	Table, values are instances.		Hetero Neural Network Model, consists of model-meta and model-param.
Homo Secure Boosting	HomoSecureBoost	Build homo secure boosting model through multiple parties	Table, values are instances.	Table, values are instances.		SecureBoost Model, consists of model-meta and model-param.
Homo OneHot Encoder	HomoOneHotEncoder	Build homo onehot encoder model through multiple parties.	Table, values are instances.	Transformed Table with new header.		Feature-name mapping between original header and new header.
Data Split	Data Split	Split one data table into 3 tables by given ratio or count	Table, values are instances.	3 Tables, values are instance.
Column Expand	Column Expand	Add arbitrary number of columns with user-provided values.	Table, values are raw data.	Transformed Table with added column(s) and new header.		Column Expand Model
Secure Information Retrieval	Secure Information Retrieval	Securely retrieves information from host through oblivious transfer	Table, values are instance	Table, values are instance
Hetero Federated Transfer Learning	Hetero FTL	Build Hetero FTL Model Between 2 party	Table, values are instance			Hetero FTL Model
Hetero KMeans	Hetero KMeans	Build Hetero KMeans model through multiple parties	Table, values are instance	Table, values are instance; Arbier outputs 2 Tables		Hetero KMeans Model
PSI	PSI module	Compute PSI value of features between two table	Table, values are instance			PSI Results
Data Statistics	Data Statistics	This component will do some statistical work on the data, including statistical mean, maximum and minimum, median, etc.	Table, values are instance	Table		Statistic Result
Scorecard	Scorecard	Scale predict score to credit score by given scaling parameters	Table, values are predict score	Table, values are score results
Sample Weight	Sample Weight	Assign weight to instances according to user-specified parameters	Table, values are instance	Table, values are weighted instance
Feldman Verifiable Sum	Feldman Verifiable Sum	This component will sum multiple privacy values without exposing data	Table, values are addend	Table, values are sum results

Secure Protocol

• Encrypt
  • Paillier encryption
  • Affine Homomorphic Encryption
  • IterativeAffine Homomorphic Encryption
  • RSA encryption
  • Fake encryption
• Encode
• Diffne Hellman Key Exchange
• SecretShare MPC Protocol(SPDZ)
• Oblivious Transfer
• Feldman Verifiable Secret Sharing

Params

.. automodule:: federatedml.param
   :autosummary:
   :members: