  To handle the data explosion in the era of internet of things (IoT), it is of
interest to investigate the decentralized network, with the aim at relaxing the
burden to central server along with keeping data privacy. In this work, we
develop a fully decentralized federated learning (FL) framework with an inexact
stochastic parallel random walk alternating direction method of multipliers
(ISPW-ADMM). Performing more communication efficient and enhanced privacy
preservation compared with the current state-of-the-art, the proposed ISPW-ADMM
can be partially immune to the impacts from time-varying dynamic network and
stochastic data collection, while still in fast convergence. Benefits from the
stochastic gradients and biased first-order moment estimation, the proposed
framework can be applied to any decentralized FL tasks over time-varying
graphs. Thus to further demonstrate the practicability of such framework in
providing fast convergence, high communication efficiency, and system
robustness, we study the extreme learning machine(ELM)-based FL model for
robust beamforming (BF) design in UAV communications, as verified by the
numerical simulations.
