Privacy-Preserving Consensus for Multiagent Networks via Weight Iteration

This paper proposes a new consensus algorithm for multiagent networks (MANs) operating under deception attacks and privacy disclosures. First, to address the exposure of real-time agent state information during transmission, a time-varying weighted iteration mechanism is developed based on the PushSum algorithm, ensuring secure protection of agent privacy. Second, to mitigate the impact of external attackers on consensus, the weighted iteration mechanism is integrated with the W-MSR algorithm, enabling secure system convergence. Finally, through mathematical theory analysis proved that the algorithm can effectively protect the privacy of agent initial state information and ensure the MAN to achieve resilient consensus under the deception attacks.