Resilient Constrained Optimization in Multi-Agent Systems with Improved Guarantee on Approximation Bounds

This paper considers resilient decentralized constrained optimization in multi-agent systems where some agents due to cyberattacks become adversaries. We show that the proposed method is resilient despite the persistent influence of up to F anonymous adversaries in the complete graphs. Our approach provides a better approximation of the optimal solution than the current literature. If the agents’ objectives are 2F redundant, then the algorithm converges to the optimal solution. In addition to current literature, we consider a constrained optimization problem. Finally, we present numerical simulations to corroborate the theoretical analysis.