### [中文](./README_zh.md)
[![License](https://img.shields.io/github/license/paritybit-ai/XFL)](https://opensource.org/licenses/Apache-2.0)
[![Documentation Status](https://readthedocs.org/projects/xfl/badge/?version=latest)](https://xfl.readthedocs.io/en/latest/?badge=latest)
[![coverage report](https://git.basebit.me/bag1/federated-learning/badges/master/coverage.svg)](https://git.basebit.me/bag1/federated-learning/-/commits/master)
XFL is a high-performance, high-flexibility, high-applicability, lightweight, open and easy-to-use Federated Learning framework.
It supports a variety of federation models in both horizontal and vertical federation scenarios.
To enable users to jointly train model legally and compliantly to unearth the value of their data, XFL adopts homomorphic encryption,
differential privacy, secure multi-party computation and other security technologies to protect users' local data from leakage,
and applies secure communication protocols to ensure communication security.
# Highlights
- High-performance algorithm library
- Comprehensive algorithms: support a variety of mainstream horizontal/vertical federation algorithms.
- Excellent performance: significantly exceeds the average performace of federated learning products.
- Network optimization: adapt to high latency, frequent packet loss, and unstable network environments.
- Flexible deployment
- parties: support two-party/multi-party federated learning.
- schedulering: any participant can act as a task scheduler.
- hardware: support CPU/GPU/hybrid deployment.
- Lightweight, open and easy to use:
- Lightweight: low requirements on host performance.
- Open: support mainstream machine learning frameworks such as Pytorch and Tensorflow, and user can conveniently design their own horizontal federation models.
- Easy to use: able to run in both docker environment and Conda environment.
# Quick Start Demo
Running in standalone mode
```shell
# create and activate the virtual environment
conda create -n xfl python=3.9.7
conda activate xfl
# install redis and other dependencies
# Ubuntu
apt install redis-server
# CentOS
yum install epel-release
yum install redis
# MacOS
brew install redis
brew install coreutils
# install python dependencies
# update pip
pip install -U pip
# install dependencies
pip install -r requirements.txt
# set permission
sudo chmod 755 /opt
# enter the project directory
cd ./demo/vertical/logistic_regression/2party
# start running the demo
sh run.sh
```
- [Quick Start](./docs/en/source/tutorial/usage.md)
# Document
- [Document](https://xfl.readthedocs.io/en/latest)
## Tutorial
- [Introduction](./docs/en/source/tutorial/introduction.md)
## Algorithms
- [List of Availble Algorithms](./docs/en/source/algorithms/algorithms_list.rst)
- [Cryptographic Algorithms](./docs/en/source/algorithms/cryptographic_algorithm.rst)
- [Differential Privacy](./docs/en/source/algorithms/differential_privacy.rst)
## Development
- [API](./docs/en/source/development/api.rst)
- [Developer Guide](./docs/en/source/development/algos_dev.rst)
# License
[Apache License 2.0](./LICENSE)
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隐私保护是数字经济的安全底座,如何在保障用户数据隐私的同时提供高质量连通服务,成为数字经济时代的重要技术课题。为破解隐私保护与数据应用的两难,以“数据不动模型动”为理念的联邦学习框架应运而生,其通过用户数据不出本地的方式完成云端模型训练,实现了“数据可用不可见”。近年来,联邦学习成为隐私保护计算主流技术之一。 然而,随着需应用隐私保护计算的场景和行业日趋多元,涉及到的数据类型日趋丰富,已有联邦学习框架难以灵活高效地满足现实中越来越复杂的计算需要,需从注重“可用”到注重“好用”。 为解决上述挑战,达摩院智能计算实验室研发了新型联邦学习框架FederatedScope,该框架使用事件驱动的编程范式来构建联邦学习,即将联邦学习看成是参与方之间收发消息的过程,通过定义消息类型以及处理消息的行为来描述联邦学习过程。通过这一方式,FederatedScope实现了支持在丰富应用场景中进行大规模、高效率的联邦学习异步训练。
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一个高效易用的联邦学习框架.rar (856个子文件)
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