Can Competition Outperform Collaboration? The Role of Misbehaving Agents

TL;DR

This paper explores how introducing competition into multi-agent systems can enhance resilience against misbehaving agents, outperforming traditional filtering methods through analytical and numerical evidence.

Contribution

It proposes a novel competition-based approach to resilient distributed optimization, demonstrating its advantages over existing filtering strategies and analyzing the effects of network topology.

Findings

Competition can improve resilience in multi-agent systems.

A trade-off exists between collaboration and competition.

The approach outperforms Weighted Mean Subsequence Reduced algorithms.

Abstract

We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave. In contrast to commonly adopted filtering strategies, we draw inspiration from phenomena modeled through the Friedkin-Johnsen dynamics and argue that adding competition to the mix can improve resilience in the presence of misbehaving agents. Our intuition is corroborated by analytical and numerical results showing that (i) there exists a nontrivial trade-off between full collaboration and full competition and (ii) our competition-based approach can outperform state-of-the-art algorithms based on Weighted Mean Subsequence Reduced. We also study impact of communication topology and connectivity on resilience, pointing out insights to robust network design.