This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs). Under the assumption that the fronthaul links connecting APs to CS have finite capacity, a rate-splitting transmission at IoT devices (IDs) is proposed which enables hybrid edge and cloud decoding of split uplink messages. The problem of completion time minimization for FL is tackled by optimizing the rate-splitting transmission and fronthaul quantization strategies along with training hyperparameters such as precision and iteration numbers. Numerical results show that the proposed rate-splitting transmission achieves notable gains over benchmark schemes which rely solely on edge or cloud decoding.

翻译：这项工作研究在雾无线电接入网络上联合学习(FL),在这个网络中,多个互联网装置通过分布式接入点与云层服务器(CS)通信,通过分布式接入点合作学习一个共享的机器学习模式。根据将APs与CS连接的正面连接具有有限容量的假设,提议在IoT设备上进行分速传输,使混合边缘和云分解链接信息解码。通过优化分速传输和前厅量化战略,以及精度和迭代数等培训超参数,解决了FL的完成时间最小化问题。数字结果显示,拟议的分速传输比完全依赖边缘或云分解的基准计划取得了显著收益。