Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning

Federated edge learning (FEEL) has emerged as a revolutionary paradigm for development of AI services at the edge of 6G wireless networks because it supports collaborative model training for a large 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.