FedQV: Leveraging Quadratic Voting in Federated Learning

arXiv:2401.01168v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv context cs.cr cs.lg decision elections federated learning global labels major making parties public shares train type voting