Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
This addresses efficiency and privacy challenges in AI training at the edge for 6G networks, but appears incremental as it reviews existing RIS solutions.
The paper tackles the bottleneck of model communication in over-the-air federated edge learning (FEEL), which suffers from straggler issues, high overhead, and privacy risks, by leveraging reconfigurable intelligent surfaces (RIS) to enhance performance.
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices. However, model communication over wireless channels, especially in uplink model uploading of FEEL, has been widely recognized as a bottleneck that critically limits the efficiency of FEEL. Although over-the-air computation can alleviate the excessive cost of radio resources in FEEL model uploading, practical implementations of over-the-air FEEL still suffer from several challenges, including strong straggler issues, large communication overheads, and potential privacy leakage. In this article, we study these challenges in over-the-air FEEL and leverage reconfigurable intelligent surface (RIS), a key enabler of future wireless systems, to address these challenges. We study the state-of-the-art solutions on RIS-empowered FEEL and explore the promising research opportunities for adopting RIS to enhance FEEL performance.