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

Federated learning (a system where multiple computers train an AI model together while keeping their data private) can be unfair to some participants and vulnerable to attacks where bad actors tamper with the process. FairRoP is a new method that uses adaptive client selection (choosing which computers to include based on their trustworthiness) and a bandit algorithm (a technique for balancing exploration and exploitation in decision-making) to improve both fairness and robustness against attacks. The approach combines three components: fairness awareness, attack detection, and q-Balance to handle the different challenges involved.