Distributed Learning over IRS-Assisted Intelligent Wireless Networks

Driven by the new era of big data and artificial intelligence (AI), as well as the increasing demands for the privacy protection, how to deployment the AI on wireless networks is drawing increasing attention. In this paper, we investigate the distributed learning mechanism of hosting AI over intelligent reflecting surface (IRS)-assisted wireless networks, where IRS is utilized to enhance communication in a cost-effective and energy-efficient manner. Firstly, a distributed learning framework is formulated based on the alternating direction method of multipliers (ADMM) to achieve the parallel processing of the objective function. Specifically, in the proposed architecture each user updates the learning model with its own data and uploads it to the global model through wireless networks. Thence, a joint passive phase shift of IRS and user scheduling scheme based on a metric of efficiency-efficacy weighted sum (EEWS) is formulated to explore both the learning efficiency and efficacy. In addition, aiming at improving the one-round learning efficiency, a grouping-based suboptimal solution about IRS’s phase is adopted to realize the max-min fair transmission. Simulation results demonstrate the relationship among the number of users involved, the scale of IRS and the learning performance.