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.
翻译:联邦边缘学习(FEEL)已成为在6G无线网络边缘发展AI服务的革命范式,因为它支持大量移动设备的合作模式培训;然而,无线频道的示范通信,特别是上传上感觉的上行模式,被广泛视为严重限制感觉效率的瓶颈;虽然超空计算可以减轻感知模式上传中无线电资源的过度成本,但超空感觉的实际实施仍然受到若干挑战,包括强烈的分层问题、大型通信管理以及潜在的隐私渗漏。 在文章中,我们研究了在超空感觉和利用可重新配置智能表面(RIS)的这些挑战,这是未来无线系统的关键推动者,我们研究了关于RIS动力感觉的先进解决方案,并探索了采用RIS提高情感表现的有希望的研究机会。