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arXiv Machine Learning By Pravija Raj P V, Ashish Gupta, Andrea Augello, Sajal K. Das

Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities

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The paper reviews the state of robustness in Federated Learning (FL), highlighting its vulnerability to performance degradation, data theft, and aggregation attacks. It presents a comprehensive framework that includes a threat-centric view of attack surfaces, a taxonomy of robust aggregation methods (distinguishing outcome‑centric from security‑centric approaches), and a layered taxonomy of defensive strategies. The authors also scrutinize current evaluation practices and outline key applications and open research challenges to steer future work.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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