A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
Optimization and Control
2024-12-20 v1 Distributed, Parallel, and Cluster Computing
Machine Learning
Multiagent Systems
Systems and Control
Systems and Control
Abstract
In this work we present a randomized gossip algorithm for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes. We give iteration complexity bounds for the method and perform extensive numerical experiments.
Cite
@article{arxiv.1901.09367,
title = {A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion},
author = {Filip Hanzely and Jakub Konečný and Nicolas Loizou and Peter Richtárik and Dmitry Grishchenko},
journal= {arXiv preprint arXiv:1901.09367},
year = {2024}
}
Comments
NeurIPS 2018, Privacy Preserving Machine Learning Workshop (camera ready version). The full-length paper, which includes a number of additional algorithms and results (including proofs of statements and experiments), is available in arXiv:1706.07636