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The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-artmethod for Byzantine-resilient design of decentralized multi-robot systems, isbased on discarding outliers received over Linear Consensus Protocol (LCP).Although W-MSR provides well-understood theoretical guarantees relating robustnetwork connectivity to the convergence of the underlying consensus, the methodcomes with several limitations preventing its use at scale: (1) the number ofByzantine robots, F, to tolerate should be known a priori, (2) the requirementthat each robot maintains 2F+1 neighbors is impractical for large F, (3)information propagation is hindered by the requirement that F+1 robotsindependently make local measurements of the consensus property in order forthe swarm's decision to change, and (4) W-MSR is specific to LCP and does notgeneralize to applications not implemented over LCP. In this work, we propose aDecentralized Blocklist Protocol (DBP) based on inter-robot accusations.Accusations are made on the basis of locally-made observations of misbehavior,and once shared by cooperative robots across the network are used as input to agraph matching algorithm that computes a blocklist. DBP generalizes toapplications not implemented via LCP, is adaptive to the number of Byzantinerobots, and allows for fast information propagation through the multi-robotsystem while simultaneously reducing the required network connectivity relativeto W-MSR. On LCP-type applications, DBP reduces the worst-case connectivityrequirement of W-MSR from (2F+1)-connected to (F+1)-connected and the number ofcooperative observers required to propagate new information from F+1 to just 1observer. We demonstrate empirically that our approach to Byzantine resiliencescales to hundreds of robots on cooperative target tracking, timesynchronization, and localization case studies.

Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations