Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
It addresses the challenge of incentivizing participation in privacy-sensitive domains like healthcare or finance by ensuring provable benefits for all clients.
The paper tackles the problem of designing federated learning protocols that balance privacy and model accuracy, providing necessary and sufficient conditions for mutual benefits and demonstrating improved accuracy in synthetic experiments.
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 for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients' utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments.