Fairis shows that weighting a client by a security parameter minus its local fairness score makes its aggregation weight strictly decrease with reported bias, while keeping every client's weight positive.
In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
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Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Fairis shows that weighting a client by a security parameter minus its local fairness score makes its aggregation weight strictly decrease with reported bias, while keeping every client's weight positive.