Decentralized diffusion-based learning under non-parametric limited
  prior knowledge
Paweł Wachel Krzysztof Kowalczyk Cristian R. Rojas
We study the problem of diffusion-based network learning of a nonlinear
phenomenon, $m$, from local agents' measurements collected in a noisy
environment. For a decentralized network and information spreading merely
between directly neighboring nodes, we propose a non-parametric learning
algorithm, that avoids raw data exchange and requires only mild \textit{a
priori} knowledge about $m$. Non-asymptotic estimation error bounds are derived
for the proposed method. Its potential applications are illustrated through
simulation experiments.