This work explores the distributed state estimation problem for an uncertain, nonlinear, and continuous-time system. Given a sensor network, each agent is assigned deep neural network (DNN) that used to approximate system's dynamics. Each updates weights of their DNN through multiple timescale approach, i.e., outer layer are updated online with Lyapunov-based gradient descent update law, inner ...