A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence
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DOI：
10.1109/cdc49753.2023.10383541
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发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Yongqiang Wang
中科院分区：
文献类型：
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作者：
Yongqiang Wang
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 equivalent seeking. We propose a new dynamic average consensus algorithm that is robust to持续和独立的信息共享噪声是为了获得差异性保护的目的。算法也可以用来抵消交流缺陷。
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 ϵ-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.