Secure multiple adaptive kernel diffusion LMS algorithm for distributed estimation over sensor networks

This paper introduces a kernel-based approach to enhance the security of distributed estimation in the presence of adversary links. Adversary links often degrade distributed recovery algorithm performance in distributed estimation. The authors propose secure distributed estimation algorithms employing an adaptive kernel and adaptive combination coefficients derived from it. The authors’ method includes a multiple kernel approach with varied widths and a heuristic formula for combination coefficients, improving performance in the presence of adversary links. Additionally, the approach is extended to single exponential kernels with fixed and adaptive widths, treating them as special cases. The multiple kernel method is used because it provides more degrees of freedom compared to a single kernel, leading to better results. Simulation results show that the proposed multiple kernel approach achieves performance close to the diffusion least mean square algorithm in the absence of attacks. The adaptive nature of the kernel and coefficients enhances algorithm robustness, making it promising for secure distributed estimation in the presence of adversary links.