Dynamics-based algorithm-level privacy preservation for push-sum average consensus

doi:10.1016/j.knosys.2026.115812

Dynamics-based algorithm-level privacy preservation for push-sum average consensus

2026-03-20

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DOI：10.1016/j.knosys.2026.115812

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In the intricate dance of multi-agent systems, achieving average consensus is not just vital–it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement through individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may reveal sensitive information. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in several initial iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. The designed algorithms are fundamentally different compared to differential privacy-based algorithms that enable privacy preservation via sacrificing consensus performance. Finally, numerical experiments validate the correctness of the theoretical findings.

Keywords:

Average Consensus

Privacy Preservation

Multi-Agent Systems

Push-Sum Algorithm

Robust Consensus Dynamics

Dynamics-based algorithm-level privacy preservation for push-sum average consensus

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