Competition-Based Resilience in Distributed Quadratic Optimization

We propose a competition-based approach to resilient distributed optimization with quadratic costs in Networked Control Systems (e.g., wireless sensor network, power grid, robotic team) where a fraction of agents may misbehave (through, e.g., hacking or power outage). Departing from classical filtering strategies proposed in literature, and inspired by a game-theoretic interpretation of consensus, we propose to introduce competition among normally behaving agents as a mean to enhance resilience against malicious attacks. Our proposal is supported by formal and heuristic results which show that i) there exists a nontrivial trade-off between blind collaboration and full competition and ii) the proposed approach can outperform standard techniques based on the algorithm Mean Subsequence Reduced.