Competition-Based Resilience in Distributed Quadratic Optimization
(2022) 61st IEEE Conference on Decision and Control, CDC 2022 In Proceedings of the IEEE Conference on Decision and Control 2022-December. p.6454-6459- 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... (More)
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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- author
- Ballotta, Luca ; Como, Giacomo LU ; Shamma, Jeff S. and Schenato, Luca
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2022 IEEE 61st Conference on Decision and Control, CDC 2022
- series title
- Proceedings of the IEEE Conference on Decision and Control
- volume
- 2022-December
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 61st IEEE Conference on Decision and Control, CDC 2022
- conference location
- Cancun, Mexico
- conference dates
- 2022-12-06 - 2022-12-09
- external identifiers
-
- scopus:85134382614
- ISSN
- 2576-2370
- 0743-1546
- ISBN
- 9781665467612
- DOI
- 10.1109/CDC51059.2022.9993083
- project
- Dynamics of Complex Socio-Technological Network Systems
- language
- English
- LU publication?
- no
- additional info
- 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.
- id
- 96c1aaa6-e6e2-4d28-a833-5c7f82cb6863
- date added to LUP
- 2023-03-21 13:19:37
- date last changed
- 2024-04-18 19:31:54
@inproceedings{96c1aaa6-e6e2-4d28-a833-5c7f82cb6863,
abstract = {{<p>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.</p>}},
author = {{Ballotta, Luca and Como, Giacomo and Shamma, Jeff S. and Schenato, Luca}},
booktitle = {{2022 IEEE 61st Conference on Decision and Control, CDC 2022}},
isbn = {{9781665467612}},
issn = {{2576-2370}},
language = {{eng}},
pages = {{6454--6459}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the IEEE Conference on Decision and Control}},
title = {{Competition-Based Resilience in Distributed Quadratic Optimization}},
url = {{http://dx.doi.org/10.1109/CDC51059.2022.9993083}},
doi = {{10.1109/CDC51059.2022.9993083}},
volume = {{2022-December}},
year = {{2022}},
}