Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems

Michal Yemini, Angelia Nedic, Stephanie Gil, Andrea J. Goldsmith

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

2 Scopus citations

Abstract

Enhancing resilience in distributed networks in the face of malicious agents is an important problem for which many key theoretical results and applications require further development and characterization. This work develops a new algorithmic and analytical framework for achieving resilience to malicious agents in distributed optimization problems where a legitimate agent's dynamic is influenced by the values it receives from neighboring agents and its own self-serving target function. We show that by utilizing stochastic values of trust between agents it is possible to recover convergence to the system's global optimal point even in the presence of malicious agents. Additionally, we provide expected convergence rate guarantees in the form of an upper bound on the expected squared distance to the optimal value. Finally, we present numerical results that validate the analytical convergence guarantees we present in this paper even when the malicious agents are the majority of agents in the network.

Original languageEnglish
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4185-4192
Number of pages8
ISBN (Electronic)9781665467612
DOIs
StatePublished - 2022
Externally publishedYes
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: 6 Dec 20229 Dec 2022

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2022-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference61st IEEE Conference on Decision and Control, CDC 2022
Country/TerritoryMexico
CityCancun
Period6/12/229/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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