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.

翻译：跨机构联邦学习（FL）允许数据所有者通过利用彼此私有数据集的优势来训练精确的机器学习模型。然而，协作带来的模型精度优势常因隐私防御措施而受到削弱。因此，为激励客户在隐私敏感领域参与协作，联邦学习协议需要在隐私保证与终端模型精度之间达成微妙的平衡。本文研究了服务器何时以及如何设计一种可证明对所有参与者均有利的联邦学习协议。首先，我们在均值估计与凸随机优化的背景下，给出了互惠协议存在的充分必要条件。同时，针对对称隐私偏好的场景，我们推导出能够最大化客户端总效用的协议方案。最后，我们设计了可最大化终端模型精度的协议，并通过合成实验验证了其优越性。