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

• URL: http://arxiv.org/abs/2109.02353v1
• Date: Mon, 6 Sep 2021 10:44:54 GMT
• Title: Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
• Authors: Hang Liu, Zehong Lin, Xiaojun Yuan, and Ying-Jun Angela Zhang
• Abstract summary: Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks. 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. 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.
• Score: 21.027054663312228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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