Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach

TL;DR

This paper proposes a novel self-triggered hybrid detection method to achieve resilient distributed optimization in multi-agent systems under Byzantine attacks, balancing attack detection and communication efficiency.

Contribution

It introduces a hybrid detection framework combining error thresholds and triggering intervals for Byzantine attack identification in continuous-time multi-agent systems.

Findings

Effective attack detection with reduced communication triggers.

Guarantees optimization accuracy despite neighbor isolation.

Applicable to heterogeneous multi-agent systems under adversarial threats.

Abstract

How to achieve precise distributed optimization despite unknown attacks, especially the Byzantine attacks, is one of the critical challenges for multiagent systems. This paper addresses a distributed resilient optimization for linear heterogeneous multi-agent systems faced with adversarial threats. We establish a framework aimed at realizing resilient optimization for continuous-time systems by incorporating a novel self-triggered hybrid detection approach. The proposed hybrid detection approach is able to identify attacks on neighbors using both error thresholds and triggering intervals, thereby optimizing the balance between effective attack detection and the reduction of excessive communication triggers. Through using an edge-based adaptive self-triggered approach, each agent can receive its neighbors' information and determine whether these information is valid. If any neighbor prove invalid, each normal agent will isolate that neighbor by disconnecting communication along that specific edge. Importantly, our adaptive algorithm guarantees the accuracy of the optimization solution even when an agent is isolated by its neighbors.