# 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
FATE是由Webank的AI部门发起的开源项目，旨在提供安全的计算框架来支持联邦AI生态系统
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FATE是由Webank的AI部门发起的开源项目，旨在提供安全的计算框架来支持联邦AI生态系统 （2654个子文件） 
nginx.conf 2KB coordination_http_proxy.conf 742B coordination_grpc_proxy.conf 369B test.conf 236B main.bf3dc0a9.min.css 6KB termynal.css 2KB custom.css 798B extra.css 473B nus_wide_train_guest.csv 64.9MB feats.csv 14.84MB feats.csv 14.84MB give_credit_hetero_guest.csv 8.19MB give_credit_homo_guest.csv 7.9MB give_credit_homo_host.csv 7.9MB give_credit_hetero_host.csv 7.88MB nus_wide_validate_guest.csv 5.85MB epsilon_5k_hetero_host.csv 5.19MB give_credit_homo_test.csv 3.95MB default_credit_hetero_guest.csv 3.83MB default_credit_hetero_host.csv 2.85MB UCI_Credit_Card.csv 2.73MB UCI_Credit_Card_val.csv 2.73MB epsilon_5k_homo_host.csv 2.29MB epsilon_5k_homo_guest.csv 2.29MB give_credit_hetero_test.csv 2.05MB default_credit_homo_host_2.csv 1.99MB default_credit_homo_host_1.csv 1.99MB default_credit_homo_guest.csv 1.98MB nus_wide_train_host.csv 1.91MB tag_value_1000_140.csv 1.81MB nus_wide_validate_host.csv 1.72MB default_credit_homo_test.csv 1.49MB epsilon_5k_hetero_guest.csv 1.32MB epsilon_5k_homo_test.csv 1.14MB svmlight_host.csv 1.1MB mocked_string_data.csv 458KB adj.csv 380KB adj.csv 380KB svmlight_guest.csv 279KB breast_hetero_guest_sample_id.csv 230KB breast_hetero_guest_repeated_id.csv 224KB dvisits_hetero_host.csv 170KB breast_hetero_host_tag_value.csv 148KB mock_tag_hetero_host.csv 124KB breast_hetero_host.csv 109KB breast_homo_host.csv 83KB breast_homo_guest.csv 83KB vehicle_scale_hetero_guest.csv 75KB vehicle_scale_homo_host.csv 73KB vehicle_scale_hetero_host.csv 71KB vehicle_scale_homo_guest.csv 70KB dvisits_hetero_guest.csv 65KB motor_hetero_host.csv 62KB breast_hetero_host_sample_id.csv 58KB breast_hetero_guest.csv 57KB ionosphere_scale_hetero_host.csv 46KB motor_hetero_guest.csv 45KB breast_homo_test.csv 42KB motor_hetero_host_2.csv 37KB motor_hetero_mini_guest.csv 37KB vehicle_scale_homo_test.csv 36KB ionosphere_scale_hetero_guest.csv 33KB unified_dataset_noniid_1_1000.csv 32KB unified_dataset_noniid_2_1000_mani.csv 29KB motor_hetero_mini_host.csv 28KB motor_hetero_host_1.csv 28KB breast_hetero_mini_guest.csv 19KB breast_hetero_mini_host.csv 18KB student_hetero_guest.csv 8KB unified_dataset_validation_1000.csv 7KB student_hetero_host.csv 7KB student_homo_host.csv 5KB student_homo_guest.csv 5KB student_homo_test.csv 3KB unittest_data.csv 402B tag.csv 59B deploy 9B DISCLAIMER 3KB .gitpod.Dockerfile 5KB Dockerfile 740B fate.env 124B filenames 60B .flake8 271B
tutorial.gif 9.39MB .gitignore 1KB .gitignore 17B .gitmodules 330B
home.html 2KB home.zh.html 2KB main.html 388B Hetero-NN-Customize-Dataset.ipynb 73KB Hetero-NN-Quick-Start.ipynb 69KB Homo-NN-Quick-Start.ipynb 56KB Hetero-NN-Customize-Model.ipynb 53KB CTR-example.ipynb 52KB Homo-NN-Customize-your-Dataset.ipynb 51KB pipeline_tutorial_uploading_data_with_meta.ipynb 45KB Homo-NN-Trainer-Interfaces.ipynb 35KB Introduce-Built-In-Dataset.ipynb 34KB Homo-NN-Customize-Model.ipynb 32KB共 2654 条
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