FairMean：在标签投毒攻击下促进分布式学习中的公平性
FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks
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中文总结 AI 辅助
FairMean通过有界非递减损失权重协调公平性与鲁棒性，在标签投毒下实现公平性提升，并降低准确率方差、提高最差客户端性能。
中文摘要 AI 辅助
公平性感知的分布式学习优先考虑损失较大的客户端以减少性能差异，但标签投毒可能造成较大的损失，从而引发公平性与鲁棒性之间的冲突。我们提出FairMean来管理这一冲突。FairMean使用局部损失的有界非递减函数对客户端梯度进行加权。递增的权重优先考虑高损失客户端以促进公平性，而上限则防止由损失引起的投毒客户端梯度的过度放大。在没有标签投毒的情况下，我们表明最小化FairMean目标比最小化标准平均损失目标更有利于解的公平性。在标签投毒下，我们建立了一个平均驻点界，其攻击相关项与投毒客户端比例的平方成正比。实验表明，FairMean通过降低准确率方差并提高最差客户端准确率来促进公平性。
英文摘要
Fairness-aware distributed learning prioritizes clients with large losses to reduce performance disparities, but label poisoning can create large losses, thereby inducing a fairness--robustness conflict. We propose FairMean to manage this conflict. FairMean weights client gradients using a bounded, nondecreasing function of local loss. The increasing weights prioritize high-loss clients to promote fairness, while the upper bound prevents excessive loss-induced amplification of poisoned-client gradients. In the absence of label poisoning, we show that minimizing the FairMean objective is more conducive to solution fairness than minimizing the standard average-loss objective. Under label poisoning, we establish an average-stationarity bound whose attack-dependent term is proportional to the square of the poisoned-client fraction. Experiments show that FairMean promotes fairness by reducing accuracy variance while improving worst-client accuracy.
发表机构
- Sun Yat-Sen University（中山大学）
- Pengcheng Laboratory（鹏城实验室）
- Great Bay University（大湾区大学）
机构由 AI 辅助整理，请以论文原文为准。
