This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. To mitigate the impact of intermittent links, nodes can collaborate with their neighbors to compute local consensus which they forward to the central server. In such a setup, the communications between any pair of nodes must satisfy local differential privacy constraints. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes and, subsequently, propose a novel differentially private collaborative algorithm for DME to achieve the optimal tradeoff. Finally, we present numerical simulations to substantiate our theoretical findings.

翻译：本文考虑间歇连接网络中的分布式均值估计（Distributed Mean Estimation, DME）问题，其目标是在中央服务器的辅助下，基于分布在各个节点上的本地数据样本学习全局统计量。为减轻间歇性链路的影响，节点可与相邻节点协作计算本地共识，并将其转发至中央服务器。在此框架下，任意一对节点间的通信必须满足局部差分隐私约束。我们研究了协作中继与因节点间额外数据共享导致的隐私泄露之间的权衡关系，并据此提出一种新颖的差分隐私协作算法用于DME，以达成最优权衡。最后，通过数值仿真验证了我们的理论发现。