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

Autor:	Saha, Rajarshi, Seif, Mohamed, Yemini, Michal, Goldsmith, Andrea J., Poor, H. Vincent
Rok vydání:	2023
Předmět:	Computer Science - Information Theory Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.00035 Zobrazit plný text záznamu View this record from Arxiv