Resilient Consensus for Multi-Agent Systems under Adversarial Spreading Processes

TL;DR

This paper develops resilient consensus algorithms for multi-agent systems affected by adversarial spreading modeled by the SIR epidemic process, ensuring noninfectious agents reach consensus despite dynamic infection levels.

Contribution

It introduces a novel resilient consensus framework accounting for time-varying infections and policies, extending MSR algorithms to epidemic-influenced multi-agent networks.

Findings

Effective conditions on network structures for resilient consensus.

Validation through numerical simulations on random graphs.

Demonstrates robustness against dynamic adversarial spreading.

Abstract

This paper addresses novel consensus problems for multi-agent systems operating in an unreliable environment where adversaries are spreading. The dynamics of the adversarial spreading processes follows the susceptible-infected-recovered (SIR) model, where the infection induces faulty behaviors in the agents and affects their state values. Such a problem setting serves as a model of opinion dynamics in social networks where consensus is to be formed at the time of pandemic and infected individuals may deviate from their true opinions. To ensure resilient consensus among the noninfectious agents, the difficulty is that the number of infectious agents changes over time. We assume that a local policy maker announces the local level of infection in real-time, which can be adopted by the agent for its preventative measures. It is demonstrated that this problem can be formulated as resilient consensus in the presence of the socalled mobile malicious models, where the mean subsequence reduced (MSR) algorithms are known to be effective. We characterize sufficient conditions on the network structures for different policies regarding the announced infection levels and the strength of the epidemic. Numerical simulations are carried out for random graphs to verify the effectiveness of our approach.