FedQV: Leveraging Quadratic Voting in Federated Learning
Published in ACM SIGMETRICS/IFIP PERFORMANCE, 2024
In this paper, we propose FEDQV, a novel aggregation algorithm built upon the quadratic voting scheme, recently proposed as a better alternative to 1p1v-based elections. Our theoretical analysis establishes that FEDQV is a truthful mechanism in which bidding according to one’s true valuation is a dominant strategy that achieves a convergence rate that matches those of state-of-the-art methods. Furthermore, our empirical analysis using multiple real-world datasets validates the superior performance of FEDQV against poisoning attacks. It also shows that combining FEDQV with unequal voting “budgets” according to a reputation score increases its performance benefits even further. Finally, we show that FEDQV can be easily combined with Byzantine-robust privacy-preserving mechanisms to enhance its robustness against both poisoning and privacy attacks.