Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds

Abstract

This paper proposes an adaptive reputation-based algorithm for Byzantine-resilient vector consensus in distributed multi-agent systems. The proposed algorithm does not require a prespecified bound on the number of Byzantine neighbors as an algorithmic input. Instead, each agent evaluates neighboring behavior from observed state discrepancies and updates reputation weights using accumulated behavioral evidence. The reputation mechanism adaptively redistributes communication weights and can assign exactly zero weight to sufficiently inconsistent neighbors. These reputation weights are incorporated into the local consensus updates to suppress adversarial influence while preserving reliable neighbor interactions. The resulting algorithm couples reputation adaptation with consensus dynamics while supporting high-dimensional vector states with low per-agent computational complexity. Extensive experiments on distributed systems against classical resilient consensus methods demonstrate improved Byzantine detection accuracy and more reliable consensus performance across different attack scenarios and state dimensions.