On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference
Signal Processing
2023-11-02 v2 Multiagent Systems
Social and Information Networks
Abstract
We study the asymptotic learning rates under linear and log-linear combination rules of belief vectors in a distributed hypothesis testing problem. We show that under both combination strategies, agents are able to learn the truth exponentially fast, with a faster rate under log-linear fusion. We examine the gap between the rates in terms of network connectivity and information diversity. We also provide closed-form expressions for special cases involving federated architectures and exchangeable networks.
Keywords
Cite
@article{arxiv.2204.13741,
title = {On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference},
author = {Mert Kayaalp and Yunus Inan and Emre Telatar and Ali H. Sayed},
journal= {arXiv preprint arXiv:2204.13741},
year = {2023}
}
Comments
Accepted for publication in IEEE Transactions on Automatic Control