  This paper proposes using communication pipelining to enhance the wireless
spectrum utilization efficiency and convergence speed of federated learning in
mobile edge computing applications. Due to limited wireless sub-channels, a
subset of the total clients is scheduled in each iteration of federated
learning algorithms. On the other hand, the scheduled clients wait for the
slowest client to finish its computation. We propose to first cluster the
clients based on the time they need per iteration to compute the local
gradients of the federated learning model. Then, we schedule a mixture of
clients from all clusters to send their local updates in a pipelined manner. In
this way, instead of just waiting for the slower clients to finish their
computation, more clients can participate in each iteration. While the time
duration of a single iteration does not change, the proposed method can
significantly reduce the number of required iterations to achieve a target
accuracy. We provide a generic formulation for optimal client clustering under
different settings, and we analytically derive an efficient algorithm for
obtaining the optimal solution. We also provide numerical results to
demonstrate the gains of the proposed method for different datasets and deep
learning architectures.
