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.

翻译：我们研究基于扩散的网络学习方法，用于从局部智能体在噪声环境中收集的测量数据中学习非线性现象$m$。针对分散式网络且信息仅在直接相邻节点间传播的情况，我们提出一种非参数学习算法，该算法避免原始数据交换，仅需对$m$具备较弱的先验知识。我们推导了所提方法的非渐近估计误差界，并通过仿真实验展示了其潜在应用。