论文标题
完成雾化的联邦学习的完成时间最小
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission
论文作者
论文摘要
这项工作研究通过FOG无线电访问网络联合学习（FL），其中多个Things Internet（IoT）设备通过通过分布式接入点（APS）与云服务器（CS）进行通信，合作学习共享的机器学习模型。在假设将APS连接到CS的Fronthaul链接具有有限容量的假设，提出了在IoT设备（IDS）处的速率传输，该传输可以使混合边缘和云解码器的分裂上行链路链接消息的解码。 FL的完成时间最小化的问题是通过优化速率分类的传输和领先的量化策略以及训练超级参数（例如精度和迭代编号）来解决的。数值结果表明，所提出的速率传输可以比仅依赖边缘或云解码的基准方案获得显着的收益。
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.
