Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains

arXiv:2403.06672v1 Announce Type: cross
Abstract: Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial …

accuracy arxiv benefits client collaboration cs.cr cs.gt cs.lg data datasets defenses domains federated federated learning machine machine learning machine learning models participation privacy private protocol sensitive stat.ml strike train