  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.
