Fairis: A Provable Defense Against Fairness Poisoning in Collaborative Machine Learning
Fairis introduces a server-side reweighting scheme that provably reduces adversarial influence in collaborative ML by weighting client updates based on local fairness scores, offering monotone weight reduction against bias while maintaining positive weights for honest participants, with empirical validation on Taiwan Credit data showing 41–54% weight reduction for stealthy adversaries. Read more