Fast Convergence Rates for Distributed Non-Bayesian Learning

Angelia Nedich; Alex Olshevsky; Cesar A. Uribe

doi:10.1109/TAC.2017.2690401

Fast Convergence Rates for Distributed Non-Bayesian Learning

Angelia Nedich, Alex Olshevsky, Cesar A. Uribe

Research output: Contribution to journal › Article › peer-review

120 Scopus citations

Abstract

We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.

Original language	English (US)
Article number	7891016
Pages (from-to)	5538-5553
Number of pages	16
Journal	IEEE Transactions on Automatic Control
Volume	62
Issue number	11
DOIs	https://doi.org/10.1109/TAC.2017.2690401
State	Published - Nov 2017

Keywords

Algorithm design and analysis
Bayes methods
distributed algorithms
estimation
learning

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TAC.2017.2690401

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author = "Angelia Nedich and Alex Olshevsky and Uribe, {Cesar A.}",

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AB - We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.

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Fast Convergence Rates for Distributed Non-Bayesian Learning

Abstract

Keywords

ASJC Scopus subject areas

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