A privacy-preserving resilient algorithm for multi-agent cooperative optimization to defend against both Byzantine and eavesdropping attacks

Abstract

Multi-agent cooperative optimization offers significant benefits in terms of scalability, adaptivity, and flexibility. However, the distributed nature of multi-agent systems also makes the agents be vulnerable to external attacks. To defend against Byzantine attacks in the network, a resilient algorithm is proposed for multi-agent optimization that takes into account coupled equality constraint, which is seldom considered before in resilient algorithms. Moreover, to defend against both Byzantine and eavesdropping attacks in the network, a privacy-preserving resilient algorithm is proposed, which is innovative as it has rarely been explored before. The theoretical validity of the proposed algorithms is guaranteed through rigorous error and privacy analyses. Finally the effectiveness of the proposed algorithms are validated through simulation and contrast experiments, and the impact of different parameters on the optimization results are compared.