Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks

TL;DR

This paper introduces a robust diffusion algorithm for multi-task networks that effectively resists impulsive noise and Byzantine attacks, ensuring reliable convergence in distributed settings.

Contribution

The paper proposes the RDLMG algorithm combining Geman-McClure estimation and a mean sub-sequence reduction method for enhanced robustness against outliers and malicious attacks.

Findings

RDLMG achieves convergence of normal nodes to their ideal states.

The algorithm demonstrates robustness against impulsive interferences.

Numerical tests confirm effectiveness in localization and spectrum sensing.

Abstract

This paper studies distributed diffusion adaptation over clustered multi-task networks in the presence of impulsive interferences and Byzantine attacks. We develop a robust resilient diffusion least mean Geman-McClure-estimation (RDLMG) algorithm based on the cost function used by the Geman-McClure estimator, which can reduce the sensitivity to large outliers and make the algorithm robust under impulsive interferences. Moreover, the mean sub-sequence reduced method, in which each node discards the extreme value information of cost contributions received from its neighbors, can make the network resilient against Byzantine attacks. In this regard, the proposed RDLMG algorithm ensures that all normal nodes converge to their ideal states with cooperation among nodes. A statistical analysis of the RDLMG algorithm is also carried out in terms of mean and mean-square performances. Numerical results evaluate the proposed RDLMG algorithm in applications to multi-target localization and multi-task spectrum sensing.