Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated learning setting involving local and global models subject to user-level (joint) differential privacy. While learning a private global model induces a cost of privacy, local learning is perfectly private. We provide generalization guarantees showing that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.

翻译：大型机器学习系统往往涉及在用户群中分布的数据。 联邦学习算法通过向中央服务器而不是向整个数据集传送模型更新来利用这一结构。 在本文中,我们研究个人化的联邦化学习环境的随机优化算法,其中涉及地方和全球模式,但以用户一级(联合)的隐私为条件。在学习私人全球模式引起隐私成本的同时,当地学习完全是私人的。我们提供一般化保证,表明协调地方学习与私人集中学习可产生一种通用的有用和更好的准确性和隐私的权衡。我们用合成和现实世界数据集实验来说明我们的理论结果。