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arXiv · 2103.00148

Scalable federated machine learning with FEDn

Morgan Ekmefjord·Addi Ait-Mlouk·Sadi Alawadi·Mattias Åkesson·Prashant Singh·Ola Spjuth·Salman Toor·Andreas Hellander
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Abstract

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.

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Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Prashant Singh, Ola Spjuth, Salman Toor, Andreas Hellander. 2021-02-27. Scalable federated machine learning with FEDn. https://arxiv.org/abs/2103.00148

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