README.md 22 KB
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:
- Federated Statistic: PSI, Union, Pearson Correlation, etc.
- Federated Information Retrieval: PIR(SIR) Based OT
- Federated Feature Engineering: Feature Sampling, Feature Binning, Feature Selection, etc.
- Federated Machine Learning Algorithms: LR, GBDT, DNN, TransferLearning, UnsupervisedLearning which support Heterogeneous and Homogeneous styles, Semi-supervisedLearning which support Heterogeneous styles
- Model Evaluation: Binary | Multiclass | Regression | Clustering Evaluation, Local vs Federated Comparison.
- 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 | DataTransform | This component transforms user-uploaded data 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 | 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 | HomoFeatureBinning | 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. | |
| 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. | |
| Hetero Data Split | 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 | HomoDataSplit | Split one data table into 3 tables by given ratio or count | Table, values are instances. | 3 Tables, values are instance. | ||
| Column Expand | 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 | SecureInformationRetrieval | Securely retrieves information from host through oblivious transfer | Table, values are instance | Table, values are instance | ||
| Hetero Federated Transfer Learning | FTL | Build Hetero FTL Model Between 2 party | Table, values are instance | Hetero FTL Model | ||
| Hetero KMeans | 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 | Compute PSI value of features between two table | Table, values are instance | PSI Results | ||
| Data Statistics | 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 | Scale predict score to credit score by given scaling parameters | Table, values are predict score | Table, values are score results | ||
| Sample Weight | SampleWeight | Assign weight to instances according to user-specified parameters | Table, values are instance | Table, values are weighted instance | SampleWeight Model | |
| Feldman Verifiable Sum | FeldmanVerifiableSum | This component will sum multiple privacy values without exposing data | Table, values to sum | Table, values are sum results | ||
| Feature Imputation | FeatureImputation | This component imputes missing features using arbitrary methods/values | Table, values are Instances | Table, values with missing features filled | FeatureImputation Model | |
| Label Transform | 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 | HeteroSSHELR | Build hetero logistic regression model without arbiter | Table, values are Instances | Table, values are Instances | SSHE LR Model | |
| Hetero SSHE Linear Regression | HeteroSSHELinR | Build hetero linear regression model without arbiter | Table, values are Instances | Table, values are Instances | SSHE LinR Model | |
| Positive Unlabeled Learning | PositiveUnlabeled | Build positive unlabeled learning model | Table, values are Instances | Table, values are Instances |