SOURCE-LINKED INTELLIGENCE

FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

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 c

Read original source ↗ Open in workspace

recordType

paper

region

Global