FairMean：分布式学习中应对标签投毒的公平性方法
FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks
分布式训练里投毒攻击会让公平性机制反被利用，这篇论文给出加权方案，理论证明加实验都有，做联邦学习的人可以看看。
arXiv 论文提出 FairMean，用有界非递减的损失函数为客户端梯度加权，以化解公平性与鲁棒性的冲突。权重随本地损失增加而上升，可优先照顾高损失客户端，上限则防止被投毒客户端的梯度被过度放大。论文证明在无标签投毒时，最小化 FairMean 目标比标准平均损失目标更有利于解的公平性；在投毒场景下建立了平均平稳性界，其攻击相关项与被投毒客户端占比的平方成正比。实验显示 FairMean 能降低准确率方差并提升最差客户端的准确率。
FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks
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.