  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.
