Resilient Consensus Against Epidemic Malicious Attacks

This paper addresses novel consensus problems for multi-agent systems operating in a pandemic environment where infectious diseases are spreading. The dynamics of the diseases follows the susceptible-infected-recovered (SIR) model, where the infection induces faulty behaviors in the agents and affects their state values. To ensure resilient consensus among the noninfectious agents, the difficulty is that the number of infectious agents changes over time. We assume that a high-level policy maker announces the level of infection in realtime, which can be adopted by the agents for their 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 pandemic. Numerical simulations are carried out for random graphs to verify the effectiveness of our approach.