Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation

Distributed algorithms for multiagent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed nature of these algorithms can render these systems vulnerable to man-in-the-middle attacks that can lead to nonconvergen...

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Bibliographic Details
Published in:IEEE transactions on control of network systems Vol. 8; no. 1; pp. 282 - 294
Main Authors: Turan, Berkay, Uribe, Cesar A., Wai, Hoi-To, Alizadeh, Mahnoosh
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Control systems
Convergence
Cost function
Cyber-physical systems
distributed algorithms
gradient methods
Heuristic algorithms
multi-agent systems
Multiagent systems
Optimization
optimization methods
Resource allocation
Resource management
Robustness
ISSN:2325-5870, 2372-2533
Online Access:Get full text
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Summary:Distributed algorithms for multiagent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed nature of these algorithms can render these systems vulnerable to man-in-the-middle attacks that can lead to nonconvergence and infeasibility of resource allocation schemes. In this article, we propose attack-resilient distributed algorithms based on primal-dual optimization when Byzantine attackers are present in the system. In particular, we design attack-resilient primal-dual algorithms for static and dynamic impersonation attacks by means of robust statistics. For static impersonation attacks, we formulate a robustified optimization model and show that our algorithm guarantees convergence to a neighborhood of the optimal solution of the robustified problem. On the other hand, a robust optimization model is not required for the dynamic impersonation attack scenario and we are able to design an algorithm that is shown to converge to a near-optimal solution of the original problem. We analyze the performances of our algorithms through both theoretical and computational studies.
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ISSN:2325-5870
2372-2533
DOI:10.1109/TCNS.2020.3024485