Feder

Feder is a highly dynamic and customizable framework that can accommodate many use cases with flexibility by implementing several functionalities over different federated learning algorithms, and essentially creating a plug-and-play architecture to accommodate different use cases.

Check out the Overview of Feder section for further information, including how to Installation the project.

Note

This project is under active development.

Contents

• Overview of Feder
  • Introduction
  • Overview
• Installation
  • Source Code
  • Pip
• Tutorials
  • Running the Server and Clients
  • Federated Learning Algorithms
  • Datasets
  • Models
  • Data Distribution
  • Carbon Emissions
• Contribution to Feder
  • Reporting bugs
  • Contributing to Feder
  • Pull Request Checklist
• API

Citation

Please cite FedLab in your publications if it helps your research:

@article{
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Contacts

Contact the Feder development team through Github issues or email:

• efgh: efgh@gmail.com

• abcd: abcd@gmail.com