Package: easyfl
EasyFL: A Low-code Federated Learning Platform📘 Documentation|🛠️ InstallationIntroductionEasyFLis 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 systemProof-of-concept (POC) of new FL applicationsPrototype of industrial applicationsLearning FL implementationsWe currently focus on horizontal FL, supporting both cross-silo and cross-device FL. You can learn more about federated learning from theseresources.Major FeaturesEasy to StartEasyFL 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 FunctionalitiesEasyFL 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 ReproducibleEasyFL 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 ModesEasyFL supportsstandalone training,distributed training, andremote 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 StartedYou can refer toGet Startedfor installation andQuick Runfor the simplest way of using EasyFL.For more advanced usage, we provide a list of tutorials on:High-level APIsConfigurationsDatasetsModelsCustomize Server and ClientDistributed TrainingRemote TrainingProjects & PapersThe following publications are developed using EasyFL.Divergence-aware Federated Self-Supervised Learning,ICLR'2022.[paper]Collaborative Unsupervised Visual Representation Learning From Decentralized Data,ICCV'2021.[paper]Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification,ACMMM'2021.[paper]: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]Performance Optimization for Federated Person Re-identification via Benchmark Analysis,ACMMM'2020.[paper]Federated Unsupervised Domain Adaptation for Face Recognition,ICME'22.[paper]Join Our CommunityPlease join our community on Slack:easyfl.slack.comWe will post updated features and answer questions on Slack.LicenseThis project is released under theApache 2.0 license.CitationIf 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}
}