Active Secure Neighbor Selection in Multi-Agent Systems with Byzantine Attacks

TL;DR

This paper presents an active neighbor selection method for multi-agent systems that enhances resilience against Byzantine attacks by dynamically choosing neighbors, reconstructing spanning trees, and reducing communication overhead.

Contribution

It introduces a novel active neighbor selection framework that improves Byzantine resilience without initial connectivity assumptions and reduces communication costs.

Findings

Successfully reconstructs spanning trees among normal agents after Byzantine detection.

Achieves resilient consensus with minimal communication overhead.

Provides numerical validation of the method's effectiveness.

Abstract

This paper investigates the problem of resilient control for multi-agent systems in the presence of Byzantine adversaries via an active secure neighbor selection framework. A pre-discriminative graph is first constructed to characterize the admissible set of candidate neighbors for each agent. Based on this graph, a dynamic in-neighbor selection strategy is proposed, wherein each agent actively selects a subset of its pre-discriminative neighbors. The number of selected neighbors is adjustable, allowing for a trade-off between communication overhead and robustness, with the minimal case requiring only a single in-neighbor. The proposed strategy facilitates the reconstruction of a directed spanning tree among normal agents following the detection and isolation of Byzantine agents. It achieves resilient consensus without imposing any assumptions on the initial connectivity among normal agents. Moreover, the approach significantly reduces communication burden while maintaining resilience to adversarial behavior. A numerical example is provided to illustrate the effectiveness of the proposed method.