Resilient Distributed Estimation Through Adversary Detection

TL;DR

This paper introduces a resilient distributed estimation algorithm, FRDE, enabling multi-agent networks to accurately estimate parameters and detect adversarial agents, provided the network is connected and observable.

Contribution

The paper proposes the FRDE algorithm, a novel consensus+innovations estimator that ensures accurate estimation and adversary detection in adversarial multi-agent networks.

Findings

FRDE achieves almost sure consistency of estimates in non-adversarial conditions.

Uncompromised agents can detect adversaries if the network is connected and globally observable.

Numerical examples demonstrate the effectiveness of FRDE in adversarial scenarios.

Abstract

This paper studies resilient multi-agent distributed estimation of an unknown vector parameter when a subset of the agents is adversarial. We present and analyze a Flag Raising Distributed Estimator ($\mathcal{FRDE}$) that allows the agents under attack to perform accurate parameter estimation and detect the adversarial agents. The $\mathcal{FRDE}$ algorithm is a consensus+innovations estimator in which agents combine estimates of neighboring agents (consensus) with local sensing information (innovations). We establish that, under $\mathcal{FRDE}$, either the uncompromised agents' estimates are almost surely consistent or the uncompromised agents detect compromised agents if and only if the network of uncompromised agents is connected and globally observable. Numerical examples illustrate the performance of $\mathcal{FRDE}$.