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Pure Mathematics 2025
基于输出掩码的合作竞争多智能体系统二分一致性隐私保护问题研究
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Abstract:
本文研究了连续时间合作-竞争多智能体系统的隐私保护二分一致性问题。为了避免泄露网络节点的初始状态，同时实现具有合作- 竞争的网络节点的二分一致性，本文提出了一种新的基于隐私保护二分一致性控制算法。本文所采用的隐私保护方法为构造一个输出掩码，使智能体的内部状态不被其他智能体察觉。这与现有的差分隐私以及同态加密的隐私保护方法不同，并且创新性地使用在合作- 竞争多智能体系统中。基于所提出的隐私保护算法，本文对网络节点进行了详细的理论二分一致性和隐私保护性分析。最后，通过仿真实验验证了理论结果的有效性。
This paper investigates the privacy-preserving bipartite consensus problem in continuous-time cooperative-competitive multi-agent systems. To prevent the leakage of initial states of network nodes while achieving bipartite consensus in networks with cooperative-competitive interactions, this paper proposes a novel privacy-preserving bipartite consensus control algorithm. This paper introduces an output mask mechanism to ensure the internal states of agents remain unobservable to other nodes. This method differs from existing privacy-preserving techniques such as differential privacy and homomorphic encryption, and this paper innovatively applies this method to cooperative-competitive multi-agent systems. Based on the proposed algorithm,a detailed theoretical analysis of bipartite consensus and privacy preservation is conducted. Finally, a numerical simulation is given to validate the effectiveness of the proposed privacy-preserving bipartite consensus algorithm.
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