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共识算法，该算法对由差分隐私设计引发的信息共享噪声具有鲁棒性。动态平均共识不仅广泛用于协同控制与分布式跟踪，更是多智能体优化与分布式纳什均衡求解等众多分布式计算算法的基本构建模块。我们提出的新算法能够抵御为实现差分隐私保护而添加的持续性、独立的信息共享噪声。事实上，该算法既能保证对精确平均参考信号的收敛性（可证明），又能实现严格的ε-差分隐私（即使迭代次数趋于无穷大），据我们所知，这在平均共识算法中尚属首次。由于通信中的信道噪声可视为差分隐私噪声的特例，该算法还可用于对抗通信不完美现象。数值仿真结果验证了所提方法的有效性。