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arXiv · 2204.13741

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Mert Kayaalp·Yunus Inan·Emre Telatar·Ali H. Sayed
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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.

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Mert Kayaalp, Yunus Inan, Emre Telatar, Ali H. Sayed. 2022-04-28. On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference. https://arxiv.org/abs/2204.13741

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