Welcome to FEDn documentation¶

FEDn is a modular and model agnostic framework for hierarchical federated machine learning which scales from pseudo-distributed development to real-world production networks in distributed, heterogeneous environments.

Core Features¶

ML-framework agnostic. Model updates are treated as black-box computations. This means that it is possible to support virtually any ML model type or framework. Support for Keras and PyTorch is available out-of-the-box, and support for many other model types, including models from scikit-learn, are in active development.
Hierarchical Federated Learning for high scalability. FEDn allows for massive horizontal scaling. This is achieved by a hierarchical architecture where multiple combiners divide up the work to coordinate client updates and aggregation. Recent benchmarks show high performance both for thousands of clients in a cross-device setting and for large model updates (1GB) in a cross-silo setting, see https://arxiv.org/abs/2103.00148.
Built for real-world production scenarios. The implementation is based on proven design patterns in distributed computing and incorporates enterprise security features. A key feature is that clients do not have to expose any ingress ports.
UI to manage alliances, track training progress and follow client validations in real time. FEDn has a UI that lets you efficiently track events, training progress, visualize model validations accross the federation, and monitor both client and server performance.

Getting started¶

Prerequisites¶

Quick start¶

The quickest way to get started with FEDn is by trying out the MNIST Keras example. Alternatively, you can start the base services along with combiner and reducer as it follows.

docker-compose up -d

Distributed deployment¶

We provide instructions for a distributed reference deployment here: Distributed deployment.

Where to go from here¶

Making contributions¶

All pull requests will be considered and are much appreciated. Reach out to one of the maintainers if you are interested in making contributions, and we will help you find a good first issue to get you started. For more details please refer to our contribution guidelines.

Documentation¶

More details about the architecture and implementation:

Community support¶

Community support in available in our Discord server.

Citation¶

If you use FEDn in your research, please cite:

@article{ekmefjord2021scalable,
  title={Scalable federated machine learning with FEDn},
  author={Ekmefjord, Morgan and Ait-Mlouk, Addi and Alawadi, Sadi and {\AA}kesson, Mattias and Stoyanova, Desislava and Spjuth, Ola and Toor, Salman and Hellander, Andreas},
  journal={arXiv preprint arXiv:2103.00148},
  year={2021}
}

Organizational collaborators, contributors and supporters¶

License¶

FEDn is licensed under Apache-2.0 (see LICENSE file for full information).

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