Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation

TL;DR

This paper introduces attack-resilient distributed primal-dual algorithms for resource allocation in multi-agent systems, ensuring convergence despite Byzantine impersonation attacks using robust statistics.

Contribution

It develops novel primal-dual algorithms that are resilient to static and dynamic impersonation attacks, guaranteeing convergence to near-optimal solutions.

Findings

Algorithms guarantee convergence under attack scenarios

Proven robustness through theoretical analysis

Validated effectiveness via computational experiments

Abstract

Distributed algorithms for multi-agent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed nature of these algorithms can render these systems vulnerable to man-in-the-middle attacks that can lead to non-convergence and infeasibility of resource allocation schemes. In this paper, we propose attack-resilient distributed algorithms based on primal-dual optimization when Byzantine attackers are present in the system. In particular, we design attack-resilient primal-dual algorithms for static and dynamic impersonation attacks by means of robust statistics. For static impersonation attacks, we formulate a robustified optimization model and show that our algorithm guarantees convergence to a neighborhood of the optimal solution of the robustified problem. On the other hand, a robust optimization model is not required for the dynamic impersonation attack scenario and we are able to design an algorithm that is shown to converge to a near-optimal solution of the original problem. We analyze the performances of our algorithms through both theoretical and computational studies.