# FATE
**Repository Path**: sharlen/FATE
## Basic Information
- **Project Name**: FATE
- **Description**: 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。 它基于同态加密和多方计算（MPC）实现安全的计算协议。 它支持联邦学习体系结构和各种机器学习算法的安全计算，包括逻辑回归，深度学习和迁移学习等。
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 73
- **Created**: 2021-11-01
- **Last Updated**: 2021-11-03
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://opensource.org/licenses/Apache-2.0) [](https://checkstyle.sourceforge.io/google_style.html) [](https://checkstyle.sourceforge.io/google_style.html) [](https://travis-ci.org/FederatedAI/FATE)
[](https://codecov.io/gh/FederatedAI/FATE)
[](https://fate.readthedocs.io/en/latest/?badge=latest)
[DOC](./doc) | [Quick Start](./examples/pipeline/README.rst) | [中文](./README_zh.md)
FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.
## Federated Learning Algorithms In FATE
FATE already supports a number of federated learning algorithms, including vertical federated learning, horizontal federated learning, and federated transfer learning. More details are available in [federatedml](./python/federatedml).
## Install
FATE can be installed on Linux or Mac. Now, FATE can support：
* Native installation: standalone and cluster deployments;
* KubeFATE installation:
- Multipal parties deployment by docker-compose, which for development and test purpose;
- Cluster (multi-node) deployment by Kubernetes
### Native installation:
Software environment :jdk1.8+、Python3.6、python virtualenv、mysql5.6+
##### Standalone
FATE provides Standalone runtime architecture for developers. It can help developers quickly test FATE. Standalone support two types of deployment: Docker version and Manual version. Please refer to Standalone deployment guide: [standalone-deploy](./standalone-deploy/)
##### Cluster
FATE also provides a distributed runtime architecture for Big Data scenario. Migration from standalone to cluster requires configuration change only. No algorithm change is needed.
To deploy FATE on a cluster, please refer to cluster deployment guide: [cluster-deploy](./cluster-deploy).
### KubeFATE installation:
Using KubeFATE, FATE can be deployed by either docker-compose or Kubernetes:
* For development or testing purposes, docker-compose is recommended. It only requires Docker enviroment. For more detail, please refer to [Deployment by Docker Compose](https://github.com/FederatedAI/KubeFATE/tree/master/docker-deploy).
* For a production or a large scale deployment, Kubernetes is recommended as an underlying infrastructure to manage FATE system. For more detail, please refer to [Deployment on Kubernetes](https://github.com/FederatedAI/KubeFATE/blob/master/k8s-deploy).
More instructions can be found in [KubeFATE](https://github.com/FederatedAI/KubeFATE).
### FATE-Client Installation
FATE-client is an easy tool for interacting with FATE. We strongly recommend you install FATE-client and take its advantage to use FATE conveniently. Please refer to this [document](./python/fate_client/README.rst) for more details on FATE-Client.
## Running Tests
A script to run all the unittests has been provided in ./python/federatedml/test folder.
Once FATE is installed, tests can be run using:
> sh ./python/federatedml/test/run_test.sh
All the unittests shall pass if FATE is installed properly.
## Example Programs
### Quick Start
We have provided a tutorial for quick starting modeling task. Please refer [here](./examples/pipeline/README.rst)
### Obtain Model and Check Out Results
We provided functions such as tracking component output models or logs etc. through a tool called fate-flow. The deployment and usage of fate-flow can be found [here](./python/fate_flow/README.md)
## Doc
### API doc
FATE provides some API documents in [doc-api](https://fate.readthedocs.io/en/latest/?badge=latest)
### Develop Guide doc
How to develop your federated learning algorithm using FATE? you can see FATE develop guide document in [develop-guide](./doc/develop_guide.rst)
### Other doc
FATE also provides many other documents in [doc](./doc/). These documents can help you understand FATE better.
## Getting Involved
* Join our maillist [Fate-FedAI Group IO](https://groups.io/g/Fate-FedAI). You can ask questions and participate in the development discussion.
* For any frequently asked questions, you can check in [FAQ](https://github.com/FederatedAI/FATE/wiki).
* Please report bugs by submitting [issues](https://github.com/FederatedAI/FATE/issues).
* Submit contributions using [pull requests](https://github.com/FederatedAI/FATE/pulls)
### License
[Apache License 2.0](LICENSE)