Scalable federated machine learning with FEDn

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Published: 2022-05-19

Formatted citation

Ekmefjord M, Ait-Mlouk A, Alawadi S, Åkesson M, Stoyanova D, Spjuth O, Toor S, Hellander A. Scalable federated machine learning with FEDn.
The 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid). , 555-564. (2022). DOI: 10.1109/CCGrid54584.2022.00065

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.