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This work presents a distributed algorithm for nonlinear adaptive learning.In particular, a set of nodes obtain measurements, sequentially one per timestep, which are related via a nonlinear function; their goal is to collectivelyminimize a cost function by employing a diffusion based Kernel Least MeanSquares (KLMS). The algorithm follows the Adapt Then Combine mode ofcooperation. Moreover, the theoretical properties of the algorithm are studiedand it is proved that under certain assumptions the algorithm suffers a noregret bound. Finally, comparative experiments verify that the proposed schemeoutperforms other variants of the LMS.

A Diffusion Kernel LMS algorithm for nonlinear adaptive networks