Decentralized diffusion-based learning under non-parametric limited prior knowledge

05/05/2023

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by   Paweł Wachel, et al.

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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 a priori knowledge about m. Non-asymptotic estimation error bounds are derived for the proposed method. Its potential applications are illustrated through simulation experiments.