  Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we
propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike
traditional communication efficient FL, which regards wireless communications
as the bottleneck, we find that under many situations, the local computing
delay is comparable to the communication delay during the FL training process,
given the development of high-speed wireless transmission techniques. Thus, the
service delay in FL should be computing delay + communication delay over
training rounds. To minimize the service delay of FL, simply reducing local
computing/communication delay independently is not enough. The delay trade-off
between local computing and wireless communications must be considered.
Besides, we empirically study the impacts of local computing control and
compression strategies (i.e., the number of local updates, weight quantization,
and gradient quantization) on computing, communication and service delays.
Based on those trade-off observation and empirical studies, we develop an
optimization scheme to minimize the service delay of FL over heterogeneous
devices. We establish testbeds and conduct extensive emulations/experiments to
verify our theoretical analysis. The results show that SDEFL reduces notable
service delay with a small accuracy drop compared to peer designs.
