We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Our first contribution is to propose a simple primitive that allows for efficient implementation of several commonly used algorithms, and allows for privacy accounting that is close to that in the central setting without requiring the strong trust assumptions it entails. Second, we propose a system architecture that implements this primitive and perform a security analysis of the proposed system.

翻译：我们重新审视了当每个设备持有私有数据时，设计可扩展的私有统计与私有联邦学习协议的问题。首先，我们提出了一种简单原语，该原语能够高效实现多种常用算法，并在无需依赖中心化设置所要求的强信任假设的前提下，实现接近中心化场景的隐私核算。其次，我们提出了一种实现该原语的系统架构，并对所提系统进行了安全性分析。