Competition-Based Resilience in Distributed Quadratic Optimization

TL;DR

This paper introduces a competition-based approach to improve resilience in distributed quadratic optimization systems, demonstrating that strategic competition can outperform traditional filtering methods against malicious agents.

Contribution

It presents a novel game-theoretic framework that incorporates competition to enhance resilience in distributed optimization under attack scenarios.

Findings

Competition improves resilience compared to classical filtering.

Tradeoff exists between collaboration and competition.

Strategy outperforms Mean Subsequence Reduced algorithms.

Abstract

This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e.g., wireless sensor network, power grid, robotic team) prone to external attacks (e.g., hacking, power outage) that cause agents to misbehave. Departing from classical filtering strategies proposed in literature, we draw inspiration from a game-theoretic formulation of the consensus problem and argue that adding competition to the mix can enhance resilience in the presence of malicious agents. Our intuition is corroborated by analytical and numerical results showing that i) our strategy highlights the presence of a nontrivial tradeoff between blind collaboration and full competition, and ii) such competition-based approach can outperform state-of-the-art algorithms based on Mean Subsequence Reduced.