Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregating local models to produce the global one, shares many similarities with public decision-making, and elections in particular. In that context, a major weakness of FL, namely its vulnerability to poisoning attacks, can be interpreted as a consequence of the one person one vote (henceforth 1p1v) principle that underpins most contemporary aggregation rules.

In this paper, we introduce 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 matching that 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.

This paper has been accepted into the ACM SIGMETRICS 2024 Winter Circle, with an acceptance rate of less than 11%.

About Tianyue Chu

Tianyue Chu is a final-year PhD candidate at IMDEA Networks Institute and Universidad Carlos III de Madrid in Spain, advised by Dr.Nikolaos Laoutaris. Her research focuses on the privacy and security implications in machine learning and distributed learning. During her PhD, she has published her research in top-tier conferences such as NDSS, ACM SIGMETRICS, and the IEEE ISIT. Tianyue also serves on the TPC of IEEE SECON 2023,2024, AISCC NDSS 2024, and ACM S3 2024.

Este evento se impartirá en inglés