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.
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