Resilient multi-dimensional consensus in adversarial environment

This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, th...

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Main Authors: Yan, Jiaqi, Li, Xiuxian, Mo, Yilin, Wen, Changyun
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/163553
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1635532022-12-09T01:18:03Z Resilient multi-dimensional consensus in adversarial environment Yan, Jiaqi Li, Xiuxian Mo, Yilin Wen, Changyun School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Resilient Algorithms Robust Graphs This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an “auxiliary point” based on the received values and moves its state towards this point. Concerning this generic form, we present conditions for achieving resilient consensus and obtain a lower bound on the exponential convergence rate. Assuming that the number of malicious agents is upper bounded, two specific resilient consensus algorithms are further developed based on the obtained conditions. Particularly, the first solution, based on Helly's Theorem, achieves the consensus within the convex hull formed by the benign agents’ initial states, where the auxiliary point can be efficiently computed through linear programming. On the other hand, the second algorithm serves as a “built-in” security guarantee for standard average consensus algorithms, in the sense that its performance coincides exactly with that of the standard ones in the absence of faulty nodes while also resisting the serious influence of the misbehaving ones in adversarial environment. Some numerical examples are provided in the end to verify the theoretical results. Ministry of Education (MOE) This work was supported by the National Key Research and Development Program of China under Grant 2018AAA0101601, the Ministry of Education (MOE), Singapore under Grant MOE2020-T1-1-067, and the National Natural Science Foundation of China under Grant 62003243. 2022-12-09T01:18:03Z 2022-12-09T01:18:03Z 2022 Journal Article Yan, J., Li, X., Mo, Y. & Wen, C. (2022). Resilient multi-dimensional consensus in adversarial environment. Automatica, 145, 110530-. https://dx.doi.org/10.1016/j.automatica.2022.110530 0005-1098 https://hdl.handle.net/10356/163553 10.1016/j.automatica.2022.110530 2-s2.0-85135945255 145 110530 en MOE2020-T1-1-067 Automatica © 2022 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Resilient Algorithms
Robust Graphs
spellingShingle Engineering::Electrical and electronic engineering
Resilient Algorithms
Robust Graphs
Yan, Jiaqi
Li, Xiuxian
Mo, Yilin
Wen, Changyun
Resilient multi-dimensional consensus in adversarial environment
description This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an “auxiliary point” based on the received values and moves its state towards this point. Concerning this generic form, we present conditions for achieving resilient consensus and obtain a lower bound on the exponential convergence rate. Assuming that the number of malicious agents is upper bounded, two specific resilient consensus algorithms are further developed based on the obtained conditions. Particularly, the first solution, based on Helly's Theorem, achieves the consensus within the convex hull formed by the benign agents’ initial states, where the auxiliary point can be efficiently computed through linear programming. On the other hand, the second algorithm serves as a “built-in” security guarantee for standard average consensus algorithms, in the sense that its performance coincides exactly with that of the standard ones in the absence of faulty nodes while also resisting the serious influence of the misbehaving ones in adversarial environment. Some numerical examples are provided in the end to verify the theoretical results.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Yan, Jiaqi
Li, Xiuxian
Mo, Yilin
Wen, Changyun
format Article
author Yan, Jiaqi
Li, Xiuxian
Mo, Yilin
Wen, Changyun
author_sort Yan, Jiaqi
title Resilient multi-dimensional consensus in adversarial environment
title_short Resilient multi-dimensional consensus in adversarial environment
title_full Resilient multi-dimensional consensus in adversarial environment
title_fullStr Resilient multi-dimensional consensus in adversarial environment
title_full_unstemmed Resilient multi-dimensional consensus in adversarial environment
title_sort resilient multi-dimensional consensus in adversarial environment
publishDate 2022
url https://hdl.handle.net/10356/163553
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