Network independent rates in distributed learning

Angelia Nedich, Alex Olshevsky, César A. Uribe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

We propose a novel belief update algorithm for Distributed Non-Bayesian learning over time-varying directed graphs, where a group of agents tries to collectively select a distribution that best describes the observed data. We show that the proposed update rule, inspired by the Push-Sum algorithm, is consistent; moreover we provide an explicit characterization of its convergence rate. Our main result states that, after a transient time, all agents will concentrate their beliefs at a network independent rate. Network independent rates were not available for other consensus based distributed learning algorithms on time-varying directed graphs.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1072-1077
Number of pages6
Volume2016-July
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Externally publishedYes
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Nedich, A., Olshevsky, A., & Uribe, C. A. (2016). Network independent rates in distributed learning. In 2016 American Control Conference, ACC 2016 (Vol. 2016-July, pp. 1072-1077). [7525057] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525057