FairRoP: Robust Client Selection Scheme for Fairness-Aware Federated Learning

Summary

The paper proposes FairRoP, a client selection scheme for federated learning that aims to improve fairness and robustness at the same time. It models the problem as a multi-objective optimization solved with an ε-greedy Thompson Sampling Multi-Armed Bandit, using fairness awareness, attack detection and q-Balance submodules. Experiments on real datasets reportedly show significant gains in fairness and robustness over state-of-the-art methods, and the approach integrates with other aggregation algorithms.

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