Differential privacy for diffusion LMS algorithm over a distributed network - Zandi, Sajad; Korki, Mehdi (Digital signal processing, 2024)
This article presents a way to improve the Diffusion LMS algorithm in practical scenarios while maintaining data privacy. It focuses on reducing external errors and ensuring accurate estimation of the parameter vector through distributed and adaptive privacy protection. By combining these improvements, the overall performance of the algorithm can be significantly enhanced without compromising Mean Square Deviation (MSD). One important aspect is tuning the privacy noise power based on data sensitivity. This work provides a novel solution for improving the performance and privacy of the Diffusion LMS algorithm with implications for future research. The mean square error and evolution behavior of the algorithms are also analyzed, showing their effectiveness and robustness in preserving privacy and MSD over the network.