# Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy

## TL;DR

This paper introduces a differentially private collaborative algorithm for distributed mean estimation over intermittently connected networks, balancing privacy and accuracy through local consensus among nodes.

## Contribution

It proposes a novel differentially private algorithm for distributed mean estimation that optimally balances collaboration benefits and privacy risks in intermittent networks.

## Key findings

- The algorithm achieves the optimal privacy-accuracy tradeoff.
- Numerical simulations confirm theoretical results.
- Collaboration improves estimation accuracy under privacy constraints.

## Abstract

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.