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

Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications