We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it is also a fundamental building block in numerous distributed computation algorithms such as multi-agent optimization and distributed Nash equilibrium seeking. We propose a new dynamic average consensus algorithm that is robust to persistent and independent information-sharing noise added for the purpose of differential-privacy protection. In fact, the algorithm can ensure both provable convergence to the exact average reference signal and rigorous epsilon-differential privacy (even when the number of iterations tends to infinity), which, to our knowledge, has not been achieved before in average consensus algorithms. Given that channel noise in communication can be viewed as a special case of differential-privacy noise, the algorithm can also be used to counteract communication imperfections. Numerical simulation results confirm the effectiveness of the proposed approach.

翻译：我们提出了一种新型动态平均一致性算法，该算法对差分隐私设计引起的信息共享噪声具有鲁棒性。动态平均一致性不仅在协同控制和分布式跟踪中得到广泛应用，还是多智能体优化、分布式纳什均衡求解等众多分布式计算算法的基础构建模块。我们提出的新型动态平均一致性算法，能够抵御为实施差分隐私保护而添加的持续性独立信息共享噪声。实际上，该算法既能保证精确收敛至平均参考信号，又能实现严格的ε-差分隐私（即使迭代次数趋于无穷大时仍成立）。据我们所知，此前平均一致性算法尚未同时实现这两个目标。鉴于通信中的信道噪声可视为差分隐私噪声的特例，该算法还可用于抑制通信缺陷。数值仿真结果验证了所提方法的有效性。