This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach.

翻译：本文研究多智能体系统中的分布式聚合博弈问题。现有分布式纳什均衡求解方法要求参与者向邻居发送原始消息，导致通信负担与隐私泄露问题。为协同解决上述问题，本文提出一种利用随机压缩节省通信资源，并通过压缩引起的随机误差隐藏信息的算法。理论分析表明，即使采用激进的压缩误差来保护隐私，该算法仍能保证收敛精度。我们证明该算法通过随机量化方案实现了差分隐私。面向能耗博弈的仿真结果验证了所提方法的有效性。