We consider federated edge learning (FEEL) over wireless fading channels taking into account the downlink and uplink channel latencies, and the random computation delays at the clients. We speed up the training process by overlapping the communication with computation. With fountain coded transmission of the global model update, clients receive the global model asynchronously, and start performing local computations right away. Then, we propose a dynamic client scheduling policy, called MRTP, for uploading local model updates to the parameter server (PS), which, at any time, schedules the client with the minimum remaining upload time. However, MRTP can lead to biased participation of clients in the update process, resulting in performance degradation in non-iid data scenarios. To overcome this, we propose two alternative schemes with fairness considerations, termed as age-aware MRTP (A-MRTP), and opportunistically fair MRTP (OF-MRTP). In A-MRTP, the remaining clients are scheduled according to the ratio between their remaining transmission time and the update age, while in OF-MRTP, the selection mechanism utilizes the long term average channel rate of the clients to further reduce the latency while ensuring fair participation of the clients. It is shown through numerical simulations that OF-MRTP provides significant reduction in latency without sacrificing test accuracy.

中文翻译：

---

具有重叠通信和计算以及通道感知公平客户端调度的快速联合边缘学习

我们考虑了无线衰落信道上的联合边缘学习 (FEEL)，同时考虑了下行链路和上行链路信道延迟，以及客户端的随机计算延迟。我们通过将通信与计算重叠来加速训练过程。通过全局模型更新的喷泉编码传输，客户端异步接收全局模型，并立即开始执行本地计算。然后，我们提出了一种称为 MRTP 的动态客户端调度策略，用于将本地模型更新上传到参数服务器（PS），该服务器在任何时候都以最小的剩余上传时间调度客户端。但是，MRTP 会导致客户端在更新过程中的参与有偏差，从而导致非 iid 数据场景中的性能下降。为了克服这一点，我们提出了两种具有公平性考虑的替代方案，称为年龄感知 MRTP（A-MRTP）和机会公平 MRTP（OF-MRTP）。在A-MRTP中，剩余的客户端根据剩余传输时间和更新时间的比值进行调度，而在OF-MRTP中，选择机制利用客户端的长期平均信道速率来进一步降低时延，同时保证客户的公平参与。通过数值模拟表明，OF-MRTP 在不牺牲测试精度的情况下显着减少了延迟。选择机制利用客户端的长期平均信道速率，在保证客户端公平参与的同时进一步降低延迟。通过数值模拟表明，OF-MRTP 在不牺牲测试精度的情况下显着减少了延迟。选择机制利用客户端的长期平均信道速率，在保证客户端公平参与的同时进一步降低延迟。通过数值模拟表明，OF-MRTP 在不牺牲测试精度的情况下显着减少了延迟。