Min-Max Consensus Algorithm for Multi-agent Systems Subject to Privacy-Preserving Problem - Wang, Aijuan; Mu, Nankun; Liao, Xiaofeng (Neural Information Processing, 2018)
This paper proposes a privacy-preserving min-max consensus algorithm for discrete-time multi-agent systems, where all agents not only can reach a common state asymptotically, but also can preserve the privacy of their states at each iteration. Based on the proposed algorithm, the detailed consensus analysis is developed, including the impossibility of finite time convergence and the sufficient condition of consensus. Moreover, the privacy-preserving analysis is provided to guarantee the reliability of our privacy-preserving scheme. Finally, a numerical simulation is performed to demonstrate the correctness of our results.