为FedSSL添加KDE并测试

J_BING 66bae8d2eb [Fix] Fix FedSSL single GPU runtime. (#7) 1 anno fa
applications 66bae8d2eb [Fix] Fix FedSSL single GPU runtime. (#7) 1 anno fa
docker f919f838c2 [Release] v0.1.0 2 anni fa
docs e3b7369727 [Doc]: add README for documentation 2 anni fa
easyfl 66bae8d2eb [Fix] Fix FedSSL single GPU runtime. (#7) 1 anno fa
examples f919f838c2 [Release] v0.1.0 2 anni fa
kubernetes f919f838c2 [Release] v0.1.0 2 anni fa
protos f919f838c2 [Release] v0.1.0 2 anni fa
requirements ce4bffeb81 [Fix] Remove dependency on tqdm version 2 anni fa
.dockerignore f919f838c2 [Release] v0.1.0 2 anni fa
.gitignore 20189eced1 [Feature] Federated Unsupervised Person Re-identification (#14) 1 anno fa
.readthedocs.yaml f919f838c2 [Release] v0.1.0 2 anni fa
LICENSE f919f838c2 [Release] v0.1.0 2 anni fa
Makefile f919f838c2 [Release] v0.1.0 2 anni fa
README.md d3bf376504 [Doc]: Update publications and code release in README (#15) 1 anno fa
requirements.txt f919f838c2 [Release] v0.1.0 2 anni fa
setup.cfg f919f838c2 [Release] v0.1.0 2 anni fa
setup.py f919f838c2 [Release] v0.1.0 2 anni fa

README.md

EasyFL: A Low-code Federated Learning Platform

[![PyPI](https://img.shields.io/pypi/v/easyfl)](https://pypi.org/project/easyfl) [![docs](https://img.shields.io/badge/docs-latest-blue)](https://easyfl.readthedocs.io/en/latest/) [![license](https://img.shields.io/github/license/easyfl-ai/easyfl.svg)](https://github.com/easyfl-ai/easyfl/blob/master/LICENSE) [![maintained](https://img.shields.io/badge/Maintained%3F-YES-yellow.svg)](https://github.com/easyfl-ai/easyfl/graphs/commit-activity) [![Downloads](https://pepy.tech/badge/easyfl)](https://pepy.tech/project/easyfl) [📘 Documentation](https://easyfl.readthedocs.io/en/latest/) | [🛠️ Installation](https://easyfl.readthedocs.io/en/latest/get_started.html)

Introduction

EasyFL is an easy-to-use federated learning (FL) platform based on PyTorch. It aims to enable users with various levels of expertise to experiment and prototype FL applications with little/no coding.

You can use it for:

  • FL Research on algorithm and system
  • Proof-of-concept (POC) of new FL applications
  • Prototype of industrial applications
  • Learning FL implementations

We currently focus on horizontal FL, supporting both cross-silo and cross-device FL. You can learn more about federated learning from these resources.

Major Features

Easy to Start

EasyFL is easy to install and easy to learn. It does not have complex dependency requirements. You can run EasyFL on your personal computer with only three lines of code (Quick Start).

Out-of-the-box Functionalities

EasyFL provides many out-of-the-box functionalities, including datasets, models, and FL algorithms. With simple configurations, you simulate different FL scenarios using the popular datasets. We support both statistical heterogeneity simulation and system heterogeneity simulation.

Flexible, Customizable, and Reproducible

EasyFL is flexible to be customized according to your needs. You can easily migrate existing CV or NLP applications into the federated manner by writing the PyTorch codes that you are most familiar with.

Multiple Training Modes

EasyFL supports standalone training, distributed training, and remote training. By developing the code once, you can easily speed up FL training with distributed training on multiple GPUs. Besides, you can even deploy it to Kubernetes with Docker using remote training.

Getting Started

You can refer to Get Started for installation and Quick Run for the simplest way of using EasyFL.

For more advanced usage, we provide a list of tutorials on:

Projects & Papers

We have released the source code for the following papers under the applications folder:

:bulb: We will release the source codes of these projects in this repository. Please stay tuned.

We have been doing research on federated learning for several years, the following are our additional publications.

  • EasyFL: A Low-code Federated Learning Platform For Dummies, IEEE Internet-of-Things Journal. [paper]
  • Federated Unsupervised Domain Adaptation for Face Recognition, ICME'22. [paper]
  • Optimizing Federated Unsupervised Person Re-identification via Camera-aware Clustering, MMSP'22. [paper]

Join Our Community

Please join our community on Slack: easyfl.slack.com

We will post updated features and answer questions on Slack.

License

This project is released under the Apache 2.0 license.

Citation

If you use this platform or related projects in your research, please cite this project.

@article{zhuang2022easyfl,
  title={Easyfl: A low-code federated learning platform for dummies},
  author={Zhuang, Weiming and Gan, Xin and Wen, Yonggang and Zhang, Shuai},
  journal={IEEE Internet of Things Journal},
  year={2022},
  publisher={IEEE}
}