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arXiv Machine Learning By Motoki Nakamura

Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns

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The paper introduces S2-WEF, a dynamic free‑rider detection method for cross‑silo federated learning. It simulates weight‑evolving‑frequency (WEF) patterns of global‑model‑mimicking attacks on the server side and compares them with clients’ submitted WEFs, combining this similarity with a deviation score from mutual WEF comparisons. Using two‑dimensional clustering and per‑score classification, S2‑WEF can identify clients that switch to free‑riding during training, achieving robust detection across four datasets and five attack types without requiring proxy datasets or pre‑training.

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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