Robust multi-task distributed estimation based on generalized maximum correntropy criterion

doi:10.1088/1674-1056/acb9fc

Abstract False data injection (FDI) attacks are common in the distributed estimation of multi-task network environments, so an attack detection strategy is designed by combining the generalized maximum correntropy criterion. Based on this, we propose a diffusion least-mean-square algorithm based on the generalized maximum correntropy criterion (GMCC-DLMS) for multi-task networks. The algorithm achieves gratifying estimation results. Even more, compared to the related work, it has better robustness when the number of attacked nodes increases. Moreover, the assumption about the number of attacked nodes is relaxed, which is applicable to multi-task environments. In addition, the performance of the proposed GMCC-DLMS algorithm is analyzed in the mean and mean-square senses. Finally, simulation experiments confirm the performance and effectiveness against FDI attacks of the algorithm.

Keywords: distributed estimation generalized correntropy multi-task networks adaptive filtering

Received: 12 August 2022
Revised: 09 January 2023
Accepted manuscript online: 08 February 2023

PACS:	89.70.Cf	(Entropy and other measures of information)
	84.40.Ua	(Telecommunications: signal transmission and processing; communication satellites)
	89.70.-a	(Information and communication theory)
	43.60.Jn	(Source localization and parameter estimation)

Corresponding Authors: Ming Ye
E-mail: zmxym@swu.edu.cn

Cite this article:

Qian Hu(胡倩), Feng Chen(陈枫), and Ming Ye(叶明) Robust multi-task distributed estimation based on generalized maximum correntropy criterion 2023 Chin. Phys. B 32 068902

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Robust multi-task distributed estimation based on generalized maximum correntropy criterion

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