Finite-Time Guarantees for Byzantine-Resilient Distributed State Estimation With Noisy Measurements Academic Article uri icon

abstract

  • This work considers resilient, cooperative state estimation in unreliable multi-agent networks. A network of agents aims to collaboratively estimate the value of an unknown vector parameter, while an {em unknown} subset of agents suffer Byzantine faults. Faulty agents malfunction arbitrarily and may send out {em highly unstructured} messages to other agents in the network. As opposed to fault-free networks, reaching agreement in the presence of Byzantine faults is far from trivial. In this paper, we propose a computationally-efficient algorithm that is provably robust to Byzantine faults. At each iteration of the algorithm, a good agent (1) performs a gradient descent update based on noisy local measurements, (2) exchanges its update with other agents in its neighborhood, and (3) robustly aggregates the received messages using coordinate-wise trimmed means. Under mild technical assumptions, we establish that good agents learn the true parameter asymptotically in almost sure sense. We further complement our analysis by proving (high probability) {em finite-time} convergence rate, encapsulating network characteristics.

author list (cited authors)

  • Su, L., & Shahrampour, S.

citation count

  • 7

publication date

  • September 2020