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

TL;DR

This paper explores how reconfigurable intelligent surfaces can enhance over-the-air federated edge learning by addressing communication bottlenecks, straggler issues, and privacy concerns in wireless model uploading.

Contribution

It provides a comprehensive study of RIS-empowered FEEL, highlighting solutions and future research opportunities to improve wireless federated learning systems.

Findings

RIS can significantly reduce communication overheads in FEEL.

RIS helps mitigate straggler effects in wireless model aggregation.

RIS offers potential privacy enhancements in over-the-air FEEL.

Abstract

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.