@inproceedings{431d42fa9eb84241bbdf36db6b04cf15,
title = "Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems",
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.",
author = "Michal Yemini and Angelia Nedic and Stephanie Gil and Goldsmith, {Andrea J.}",
note = "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 = "https://doi.org/10.1109/cdc51059.2022.9992416",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4185--4192",
booktitle = "2022 IEEE 61st Conference on Decision and Control, CDC 2022",
address = "الولايات المتّحدة",
}