  This paper studies a federated edge learning system, in which an edge server
coordinates a set of edge devices to train a shared machine learning model
based on their locally distributed data samples. During the distributed
training, we exploit the joint communication and computation design for
improving the system energy efficiency, in which both the communication
resource allocation for global ML parameters aggregation and the computation
resource allocation for locally updating MLparameters are jointly optimized. In
particular, we consider two transmission protocols for edge devices to upload
ML parameters to edge server, based on the non orthogonal multiple access and
time division multiple access, respectively. Under both protocols, we minimize
the total energy consumption at all edge devices over a particular finite
training duration subject to a given training accuracy, by jointly optimizing
the transmission power and rates at edge devices for uploading MLparameters and
their central processing unit frequencies for local update. We propose
efficient algorithms to optimally solve the formulated energy minimization
problems by using the techniques from convex optimization. Numerical results
show that as compared to other benchmark schemes, our proposed joint
communication and computation design significantly improves the energy
efficiency of the federated edge learning system, by properly balancing the
energy tradeoff between communication and computation.
