  This work presents a distributed algorithm for nonlinear adaptive learning.
In particular, a set of nodes obtain measurements, sequentially one per time
step, which are related via a nonlinear function; their goal is to collectively
minimize a cost function by employing a diffusion based Kernel Least Mean
Squares (KLMS). The algorithm follows the Adapt Then Combine mode of
cooperation. Moreover, the theoretical properties of the algorithm are studied
and it is proved that under certain assumptions the algorithm suffers a no
regret bound. Finally, comparative experiments verify that the proposed scheme
outperforms other variants of the LMS.
