Competition-Based Resilience in Distributed Quadratic Optimization

Luca Ballotta; Giacomo Como; Jeff S. Shamma; Luca Schenato

doi:10.1109/CDC51059.2022.9993083

Competition-Based Resilience in Distributed Quadratic Optimization

Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

We propose a competition-based approach to resilient distributed optimization with quadratic costs in Networked Control Systems (e.g., wireless sensor network, power grid, robotic team) where a fraction of agents may misbehave (through, e.g., hacking or power outage). Departing from classical filtering strategies proposed in literature, and inspired by a game-theoretic interpretation of consensus, we propose to introduce competition among normally behaving agents as a mean to enhance resilience against malicious attacks. Our proposal is supported by formal and heuristic results which show that i) there exists a nontrivial trade-off between blind collaboration and full competition and ii) the proposed approach can outperform standard techniques based on the algorithm Mean Subsequence Reduced.

Original language	English
Title of host publication	2022 IEEE 61st Conference on Decision and Control, CDC 2022
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	6454-6459
Number of pages	6
ISBN (Electronic)	9781665467612
DOIs	https://doi.org/10.1109/CDC51059.2022.9993083
Publication status	Published - 2022
Externally published	Yes
Event	61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico Duration: 2022 Dec 6 → 2022 Dec 9

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2022-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	61st IEEE Conference on Decision and Control, CDC 2022
Country/Territory	Mexico
City	Cancun
Period	2022/12/06 → 2022/12/09

Bibliographical note

Funding Information:
This work has been partially funded by the Italian Ministry of Education, University and Research (MIUR) through the PRIN project no. 2017NS9FEY entitled “Realtime Control of 5G Wireless Networks: Taming the Complexity of Future Transmission and Computation Challenges” and through the initiative ”Departments of Excellence” (Law 232/2016). The views and opinions expressed in this work are those of the authors and do not necessarily reflect those of the funding institutions.

Publisher Copyright:
© 2022 IEEE.

Subject classification (UKÄ)

Control Engineering

Access to Document

10.1109/CDC51059.2022.9993083

Cite this

Ballotta, L., Como, G., Shamma, J. S., & Schenato, L. (2022). Competition-Based Resilience in Distributed Quadratic Optimization. In 2022 IEEE 61st Conference on Decision and Control, CDC 2022 (pp. 6454-6459). (Proceedings of the IEEE Conference on Decision and Control; Vol. 2022-December). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC51059.2022.9993083

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title = "Competition-Based Resilience in Distributed Quadratic Optimization",

abstract = "We propose a competition-based approach to resilient distributed optimization with quadratic costs in Networked Control Systems (e.g., wireless sensor network, power grid, robotic team) where a fraction of agents may misbehave (through, e.g., hacking or power outage). Departing from classical filtering strategies proposed in literature, and inspired by a game-theoretic interpretation of consensus, we propose to introduce competition among normally behaving agents as a mean to enhance resilience against malicious attacks. Our proposal is supported by formal and heuristic results which show that i) there exists a nontrivial trade-off between blind collaboration and full competition and ii) the proposed approach can outperform standard techniques based on the algorithm Mean Subsequence Reduced.",

author = "Luca Ballotta and Giacomo Como and Shamma, {Jeff S.} and Luca Schenato",

note = "Funding Information: This work has been partially funded by the Italian Ministry of Education, University and Research (MIUR) through the PRIN project no. 2017NS9FEY entitled “Realtime Control of 5G Wireless Networks: Taming the Complexity of Future Transmission and Computation Challenges” and through the initiative ”Departments of Excellence” (Law 232/2016). The views and opinions expressed in this work are those of the authors and do not necessarily reflect those of the funding institutions. Publisher Copyright: {\textcopyright} 2022 IEEE.; 61st IEEE Conference on Decision and Control, CDC 2022 ; Conference date: 06-12-2022 Through 09-12-2022",

year = "2022",

doi = "10.1109/CDC51059.2022.9993083",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

}

Ballotta, L, Como, G, Shamma, JS & Schenato, L 2022, Competition-Based Resilience in Distributed Quadratic Optimization. in 2022 IEEE 61st Conference on Decision and Control, CDC 2022. Proceedings of the IEEE Conference on Decision and Control, vol. 2022-December, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 6454-6459, 61st IEEE Conference on Decision and Control, CDC 2022, Cancun, Mexico, 2022/12/06. https://doi.org/10.1109/CDC51059.2022.9993083

Competition-Based Resilience in Distributed Quadratic Optimization. / Ballotta, Luca; Como, Giacomo; Shamma, Jeff S. et al.
2022 IEEE 61st Conference on Decision and Control, CDC 2022. IEEE - Institute of Electrical and Electronics Engineers Inc., 2022. p. 6454-6459 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2022-December).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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AU - Shamma, Jeff S.

AU - Schenato, Luca

N1 - Funding Information: This work has been partially funded by the Italian Ministry of Education, University and Research (MIUR) through the PRIN project no. 2017NS9FEY entitled “Realtime Control of 5G Wireless Networks: Taming the Complexity of Future Transmission and Computation Challenges” and through the initiative ”Departments of Excellence” (Law 232/2016). The views and opinions expressed in this work are those of the authors and do not necessarily reflect those of the funding institutions. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - We propose a competition-based approach to resilient distributed optimization with quadratic costs in Networked Control Systems (e.g., wireless sensor network, power grid, robotic team) where a fraction of agents may misbehave (through, e.g., hacking or power outage). Departing from classical filtering strategies proposed in literature, and inspired by a game-theoretic interpretation of consensus, we propose to introduce competition among normally behaving agents as a mean to enhance resilience against malicious attacks. Our proposal is supported by formal and heuristic results which show that i) there exists a nontrivial trade-off between blind collaboration and full competition and ii) the proposed approach can outperform standard techniques based on the algorithm Mean Subsequence Reduced.

AB - We propose a competition-based approach to resilient distributed optimization with quadratic costs in Networked Control Systems (e.g., wireless sensor network, power grid, robotic team) where a fraction of agents may misbehave (through, e.g., hacking or power outage). Departing from classical filtering strategies proposed in literature, and inspired by a game-theoretic interpretation of consensus, we propose to introduce competition among normally behaving agents as a mean to enhance resilience against malicious attacks. Our proposal is supported by formal and heuristic results which show that i) there exists a nontrivial trade-off between blind collaboration and full competition and ii) the proposed approach can outperform standard techniques based on the algorithm Mean Subsequence Reduced.

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Competition-Based Resilience in Distributed Quadratic Optimization

Abstract

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Bibliographical note

Subject classification (UKÄ)

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Dynamics of Complex Socio-Technological Network Systems

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Competition-Based Resilience in Distributed Quadratic Optimization

Abstract

Publication series

Conference

Bibliographical note

Subject classification (UKÄ)

Access to Document

Other files and links

Fingerprint

Projects

Dynamics of Complex Socio-Technological Network Systems

Cite this