Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic aspects of the problem. However, there is still a lack of federated machine learning frameworks that focus on fundamental aspects such as scalability, robustness, security, and performance in a geographically distributed setting. To bridge this gap we have designed and developed the FEDn framework. A main feature of FEDn is to support both cross-device and cross-silo training settings. This makes FEDn a powerful tool for researching a wide range of machine learning applications in a realistic setting.
@article{arxiv.2103.00148,
title = {Scalable federated machine learning with FEDn},
author = {Morgan Ekmefjord and Addi Ait-Mlouk and Sadi Alawadi and Mattias Åkesson and Prashant Singh and Ola Spjuth and Salman Toor and Andreas Hellander},
journal= {arXiv preprint arXiv:2103.00148},
year = {2022}
}