Publication

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan
2024
Journal Articles

Abstract

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing problem under linear and log-linear combination rules. 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.

Official source

https://infoscience.epfl.ch/entities/publication/79522d7a-2a0e-4330-b7e7-be85c39cbc98

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On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

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