# Federated Machine Learning [[中文](README.zh.md)] 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 Information Retrieval: PIR(SIR) Based OT 3. Federated Feature Engineering: Feature Sampling, Feature Binning, Feature Selection, etc. 4. Federated Machine Learning Algorithms: LR, GBDT, DNN, TransferLearning, UnsupervisedLearning which support Heterogeneous and Homogeneous styles, Semi-supervisedLearning which support Heterogeneous styles 5. Model Evaluation: Binary | Multiclass | Regression | Clustering Evaluation, Local vs Federated Comparison. 6. Secure Protocol: Provides multiple security protocols for secure multi-party computing and interaction between participants. ## Algorithm List | Algorithm | Module Name | Description | Data Input | Data Output | Model Input | Model Output | | ------------------------------------------------------------------------------ | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | ----------------------------------------------------------------------- | | [DataTransform](data_transform.md) | DataTransform | This component transforms user-uploaded data into Instance object. | Table, values are raw data. | Transformed Table, values are data instance defined [here](../../python/federatedml/feature/instance.py) | | DataTransform Model | | [Intersect](intersect.md) | 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](sample.md) | 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](scale.md) | FeatureScale | module for feature scaling and standardization. | Table，values are instances. | Transformed Table. | Transform factors like min/max, mean/std. | | | [Hetero Feature Binning](feature_binning.md) | HeteroFeatureBinning | 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](feature_binning.md) | HomoFeatureBinning | Calculate quantile binning through multiple parties | Table | Transformed Table | | Split points of each column | | [OneHot Encoder](onehot_encoder.md) | 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](feature_selection.md) | 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.md) | Union | Combine multiple data tables into one. | Tables. | Table with combined values from input Tables. | | | | [Hetero-LR](logistic_regression.md) | 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](local_baseline.md) | LocalBaseline | Wrapper that runs sklearn(scikit-learn) Logistic Regression model with local data. | Table, values are instances. | Table, values are instances. | | | | [Hetero-LinR](linear_regression.md) | 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](poisson_regression.md) | 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](logistic_regression.md) | 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](homo_nn.md) | 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](ensemble.md) | 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](ensemble.md) | 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. | | [Evaluation](evaluation.md) | Evaluation | Output the model evaluation metrics for user. | Table(s), values are instances. | | | | | [Hetero Pearson](correlation.md) | HeteroPearson | Calculate hetero correlation of features from different parties. | Table, values are instances. | | | | | [Hetero-NN](hetero_nn.md) | 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](ensemble.md) | 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](homo_onehot_encoder.md) | 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. | | [Hetero Data Split](data_split.md) | HeteroDataSplit | Split one data table into 3 tables by given ratio or count | Table, values are instances. | 3 Tables, values are instance. | | | | [Homo Data Split](data_split.md) | HomoDataSplit | 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.md) | ColumnExpand | 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](sir.md) | SecureInformationRetrieval | Securely retrieves information from host through oblivious transfer | Table, values are instance | Table, values are instance | | | | [Hetero Federated Transfer Learning](hetero_ftl.md) | FTL | Build Hetero FTL Model Between 2 party | Table, values are instance | | | Hetero FTL Model | | [Hetero KMeans](hetero_kmeans.md) | HeteroKMeans | Build Hetero KMeans model through multiple parties | Table, values are instance | Table, values are instance; Arbier outputs 2 Tables | | Hetero KMeans Model | | [PSI](psi.md) | PSI | Compute PSI value of features between two table | Table, values are instance | | | PSI Results | | [Data Statistics](statistic.md) | DataStatistics | 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.md) | 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.md) | SampleWeight | Assign weight to instances according to user-specified parameters | Table, values are instance | Table, values are weighted instance | | SampleWeight Model | | [Feldman Verifiable Sum](feldman_verifiable_sum.md) | FeldmanVerifiableSum | This component will sum multiple privacy values without exposing data | Table, values to sum | Table, values are sum results | | | | [Feature Imputation](feature_imputation.md) | FeatureImputation | This component imputes missing features using arbitrary methods/values | Table, values are Instances | Table, values with missing features filled | | FeatureImputation Model | | [Label Transform](label_transform.md) | LabelTransform | Replaces label values of input data instances and predict results | Table, values are Instances or prediction results | Table, values with transformed label values | | LabelTransform Model | | [Hetero SSHE Logistic Regression](logistic_regression.md) | HeteroSSHELR | Build hetero logistic regression model without arbiter | Table, values are Instances | Table, values are Instances | | SSHE LR Model | | [Hetero SSHE Linear Regression](linear_regression.md) | HeteroSSHELinR | Build hetero linear regression model without arbiter | Table, values are Instances | Table, values are Instances | | SSHE LinR Model | | [Positive Unlabeled Learning](positive_unlabeled.md) | PositiveUnlabeled | Build positive unlabeled learning model | Table, values are Instances | Table, values are Instances | | | ## Secure Protocol - [Encrypt](secureprotol.md#encrypt) - [Paillier encryption](secureprotol.md#paillier-encryption) - [RSA encryption](secureprotol.md#rsa-encryption) - [Hash](secureprotol.md#hash-factory) - [Diffne Hellman Key Exchange](secureprotol.md#diffne-hellman-key-exchange) - [SecretShare MPC Protocol(SPDZ)](secureprotol.md#secretshare-mpc-protocolspdz) - [Oblivious Transfer](secureprotol.md#oblivious-transfer) - [Feldman Verifiable Secret Sharing](secureprotol.md#feldman-verifiable-secret-sharing)