Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control
Kaixiang Zhang, Zhaojian Li, Yongqiang Wang, Ali Louati, Jian Chen

TL;DR
This paper introduces a novel privacy-preserving dynamic average consensus method using state decomposition, ensuring privacy against eavesdroppers while maintaining convergence, demonstrated through multi-robot formation control simulations.
Contribution
A new state decomposition scheme is proposed to protect agent privacy in dynamic consensus without sacrificing convergence performance.
Findings
The scheme prevents eavesdroppers from accurately reconstructing private signals.
Convergence of the average consensus is preserved under the proposed privacy scheme.
Effective application demonstrated in multi-robot formation control simulations.
Abstract
Dynamic average consensus is a decentralized control/estimation framework where a group of agents cooperatively track the average of local time-varying reference signals. In this paper, we develop a novel state decomposition-based privacy preservation scheme to protect the privacy of agents when sharing information with neighboring agents. Specifically, we first show that an external eavesdropper can successfully wiretap the reference signals of all agents in a conventional dynamic average consensus algorithm. To protect privacy against the eavesdropper, a state decomposition scheme is developed where the original state of each agent is decomposed into two sub-states: one succeeds the role of the original state in inter-node interactions, while the other sub-state only communicates with the first one and is invisible to other neighboring agents. Rigorous analyses are performed to show…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Marine and coastal ecosystems · Stability and Control of Uncertain Systems
