Distributed averaging with quantized communication over dynamic graphs

Mahmoud El Chamie, Ji Liu, Tamer Basar, Behcet Acikmese

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

Abstract

Distributed algorithms are the key to enabling effective large scale distributed control systems, which present many challenges due to lack of access to global information. This paper studies the effects of quantized communication among agents for distributed averaging algorithms. The agents rely on peer-to-peer interactions to exchange and update their local agreement variables that converge to the average of initial values asymptotically. Our previous work had shown that, when the underlying communication graph is static with certain types of uniform quantization effects, an optimized distributed selection of algorithm parameters results in the convergence of the agreement variables to a small neighborhood around the actual average, independent of the network size. In this paper, we extend this result to time-varying (dynamic) graphs. We first show, using an example, that the deviation from the desired average caused by quantized communication on time-varying graphs can be arbitrarily large even when the graph is connected at every iteration. This is contrary to the case without quantized communication. We then present a large class of randomized time-varying communication graphs for which the convergence to a small neighborhood around the average is guaranteed in the presence of quantized communication.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4827-4832
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period12/12/1612/14/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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