Zeroth-Order Diffusion Adaptation over Networks

Jie Chen, Sijia Liu, Pin Yu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Diffusion adaptation is an efficient strategy to perform distributed estimation over networks with streaming data. Existing diffusion-based estimation algorithms require the knowledge of analytical forms of the cost functions or their gradients associated with agents. This setting can be restrictive for practical applications where gradient calculation is difficult or systems operate in a black-box manner. Motivated by the advance of the zeroth-order (gradient-free) optimization, in this work we propose the zeroth-order (ZO) diffusion strategy using randomized gradient estimates. We also examine the stability conditions of the proposed ZO-diffusion strategy. Simulations are performed to examine properties of the algorithm and to compare it with its non-cooperative and stochastic gradient counterparts.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4324-4328
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • Diffusion adaptation
  • Distributed estimation
  • Online learning
  • Stochastic optimization
  • Zeroth-order optimization

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