Year: 2025
Type: article
Abstract: This paper proposes a new approach that enables multi-agent systems to achieve resilientconstrainedconsensus in the presence of Byzantine attacks, in contrast to existing literature that is only applicable tounconstrainedresilient consensus problems. The key enabler for our approach is a new device called a$(\gamma _{i},\alpha _{i})$-resilient convex combination, which allows normal agents in the network to utilize their locally available information to automatically isolate the impact of the Byzantine agents. Such a resilient convex combination is computable through linear programming, whose complexity scales well with the size of the overall system. By applying this new device to multi-agent systems, we introduce network and constraint redundancy conditions under which resilient constrained consensus can be achieved with an exponential convergence rate. We also provide insights on the design of a network such that the redundancy conditions are satisfied. Finally, numerical simulations and an example of safe multi-agent learning are provided to demonstrate the effectiveness of the proposed results.
Authors: Xuan Wang, Shaoshuai Mou, Shreyas Sundaram
Institutions: George Mason University, Purdue University West Lafayette
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409916886