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README
Apache-2.0

English

License Documentation Status

XFL是一个高性能、高灵活度、高适用性、轻量开放的联邦学习框架,支持横向联邦、纵向联邦等多种联邦模型,运用同态加密、差分隐私、多方安全计算等多种加密计算技术保护用户的原始数据不被泄露,使用安全通信协议保护通信安全,使用户在合法合规的基础上进行联合建模,实现数据价值。

项目特点

  • 高性能的算法库

    • 算法全面:支持多种主流横向/纵向联邦算法
    • 性能优异:性能大幅领先于信通院性能评测平均水平
    • 网络优化:在弱网络、高延迟、大量丢包、较长时间断网的情况下依然可以完成训练任务
  • 应用部署灵活

    • 计算节点灵活: 支持两方/多方计算节点部署
    • 算力调配灵活: 无论有无label,支持任何一方为发起方,支持辅助计算节点部署在任一方
    • 安装部署灵活: 支持CPU/GPU/混合部署
  • 轻量开放

    • 轻量: 对服务器性能要求低,部分算法可在性能较差的环境下运行
    • 开放:支持 PyTorch、TensorFlow、PaddlePaddle、 Jax 等主流机器学习框架,支持用户自定义横向模型
  • 支持大语言模型

快速开始

Document

Tutorial

Algorithms

Development

License

Apache License 2.0

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简介

一个高效易用的联邦学习框架 展开 收起
Python 等 2 种语言
Apache-2.0
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