论文标题
强大的动态平均共识算法，可确保差异隐私和准确的收敛性
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence
论文作者
论文摘要
我们提出了一种新的动态平均共识算法，该算法对由于差异性设计而引起的信息共享噪声是可靠的。动态平均共识不仅是在合作控制和分布式跟踪中广泛使用的，而且在众多分布式计算算法（例如多代理优化和分布式NASH平衡寻求寻求）中，它还是一个基本的构建块。我们提出了一种新的动态平均共识算法，该算法对于持续且独立的信息共享噪声是可靠的，以进行差异性私人保护。实际上，该算法可以确保可证明的融合到确切的平均参考信号和严格的Epsilon-differential隐私（即使迭代的数量趋于无穷大），据我们所知，这在平均共识算法中尚未实现。鉴于通信中的通道噪声可以看作是差异性杂志噪声的特殊情况，因此该算法也可以用来抵消通信瑕疵。数值模拟结果证实了所提出的方法的有效性。
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.
