[2604.03226] Enhancing Robustness of Federated Learning via Server Learning

Computer Science > Machine Learning arXiv:2604.03226 (cs) [Submitted on 3 Apr 2026] Title:Enhancing Robustness of Federated Learning via Server Learning Authors:Van Sy Mai, Kushal Chakrabarti, Richard J. La, Dipankar Maity View a PDF of the paper titled Enhancing Robustness of Federated Learning via Server Learning, by Van Sy Mai and 3 other authors View PDF HTML (experimental) Abstract:This paper explores the use of server learning for enhancing the robustness of federated learning against malicious attacks even when clients' training data are not independent and identically distributed. We propose a heuristic algorithm that uses server learning and client update filtering in combination with geometric median aggregation. We demonstrate via experiments that this approach can achieve significant improvement in model accuracy even when the fraction of malicious clients is high, even more than $50\%$ in some cases, and the dataset utilized by the server is small and could be synthetic with its distribution not necessarily close to that of the clients' aggregated data. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03226 [cs.LG]   (or arXiv:2604.03226v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.03226 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Van Sy Mai [view email] [v1] Fri, 3 Apr 2026 17:51:29 UTC (1,020 KB) Full-text links: Access Paper: View a PDF of the p...