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arXiv Machine Learning By Huigan Zheng, Jiaojiao Zhang, Yongxiang Liu

FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks

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FairMean is a new approach for distributed learning that addresses the conflict between fairness and robustness to label poisoning attacks. It assigns weights to client gradients based on a bounded, nondecreasing function of local loss, giving higher weight to high‑loss clients to promote fairness while limiting the influence of poisoned clients. The method is shown to improve fairness compared to standard average‑loss minimization and to reduce accuracy variance while boosting worst‑client accuracy in experiments.

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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