Distributed User-Level Private Mean Estimation

Traditionally, an item-level differential privacy framework has been studied for applications in distributed learning. However, when a client has multiple data samples, and might want to also hide its potential participation, a more appropriate notion is that of user-level privacy [1]. In this paper, we develop a distributed private optimization framework that studies the trade-off between user-level local differential privacy guarantees and performance. This is enabled by a novel distributed user-level private mean estimation algorithm using distributed private heavy-hitter estimation. We use this result to develop the privacy-performance trade-off for distributed optimization.