  Training task in classical machine learning models, such as deep neural
networks, is generally implemented at a remote cloud center for centralized
learning, which is typically time-consuming and resource-hungry. It also incurs
serious privacy issue and long communication latency since a large amount of
data are transmitted to the centralized node. To overcome these shortcomings,
we consider a newly-emerged framework, namely federated edge learning, to
aggregate local learning updates at the network edge in lieu of users' raw
data. Aiming at accelerating the training process, we first define a novel
performance evaluation criterion, called learning efficiency. We then formulate
a training acceleration optimization problem in the CPU scenario, where each
user device is equipped with CPU. The closed-form expressions for joint
batchsize selection and communication resource allocation are developed and
some insightful results are highlighted. Further, we extend our learning
framework to the GPU scenario. The optimal solution in this scenario is
manifested to have the similar structure as that of the CPU scenario,
recommending that our proposed algorithm is applicable in more general systems.
Finally, extensive experiments validate the theoretical analysis and
demonstrate that the proposed algorithm can reduce the training time and
improve the learning accuracy simultaneously.
