  Edge machine learning involves the development of learning algorithms at the
network edge to leverage massive distributed data and computation resources.
Among others, the framework of federated edge learning (FEEL) is particularly
promising for its data-privacy preservation. FEEL coordinates global model
training at a server and local model training at edge devices over wireless
links. In this work, we explore the new direction of energy-efficient radio
resource management (RRM) for FEEL. To reduce devices' energy consumption, we
propose energy-efficient strategies for bandwidth allocation and scheduling.
They adapt to devices' channel states and computation capacities so as to
reduce their sum energy consumption while warranting learning performance. In
contrast with the traditional rate-maximization designs, the derived optimal
policies allocate more bandwidth to those scheduled devices with weaker
channels or poorer computation capacities, which are the bottlenecks of
synchronized model updates in FEEL. On the other hand, the scheduling priority
function derived in closed form gives preferences to devices with better
channels and computation capacities. Substantial energy reduction contributed
by the proposed strategies is demonstrated in learning experiments.
