  Federated learning (FL) is a popular collaborative distributed machine
learning paradigm across mobile devices. However, practical FL over resource
constrained mobile devices confronts multiple challenges, e.g., the local
on-device training and model updates in FL are power hungry and radio resource
intensive for mobile devices. To address these challenges, in this paper, we
attempt to take FL into the design of future wireless networks and develop a
novel joint design of wireless transmission and weight quantization for energy
efficient FL over mobile devices. Specifically, we develop flexible weight
quantization schemes to facilitate on-device local training over heterogeneous
mobile devices. Based on the observation that the energy consumption of local
computing is comparable to that of model updates, we formulate the energy
efficient FL problem into a mixed-integer programming problem where the
quantization and spectrum resource allocation strategies are jointly determined
for heterogeneous mobile devices to minimize the overall FL energy consumption
(computation + transmissions) while guaranteeing model performance and training
latency. Since the optimization variables of the problem are strongly coupled,
an efficient iterative algorithm is proposed, where the bandwidth allocation
and weight quantization levels are derived. Extensive simulations are conducted
to verify the effectiveness of the proposed scheme.
