  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 underpinning most contemporary aggregation rules. 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.
