Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems. We study in this paper how cell-free massive MIMO can support federated edge learning. Taking advantage of the additive nature of the wireless multiple access channel, over-the-air computation is exploited, where the clients send their local updates simultaneously over the same communication resource. This approach, known as over-the-air federated learning (OTA-FL), is proven to alleviate the communication overhead of federated learning over wireless networks. Considering channel correlation and only imperfect channel state information available at the central server, we propose a practical implementation of OTA-FL over cell-free massive MIMO. The convergence of the proposed implementation is studied analytically and experimentally, confirming the benefits of cell-free massive MIMO for OTA-FL.

翻译：大规模无细胞大型IMO正在成为未来无线通信系统的一个有希望的技术,预计将提供与古典蜂窝系统相比的统一覆盖和高光谱效率。我们在本文中研究无细胞大型IMO如何支持联合边际学习。利用无线多重接入频道的添加性,利用空中计算,客户同时用同一通信资源发送本地最新消息。这种被称为超空联合学习(OTA-FL)的方法证明可以减轻无线网络联合学习的通信间接费用。考虑到频道的相互关系以及中央服务器上提供的不完善的频道状态信息,我们提议实际实施OTA-FL而非无细胞大型IMO。对拟议执行的趋同进行了分析和实验性研究,确认无细胞大型IMO对OTA-FL的好处。