  Federated learning (FL) is a new paradigm for large-scale learning tasks
across mobile devices. However, practical FL deployment over resource
constrained mobile devices confronts multiple challenges. For example, it is
not clear how to establish an effective wireless network architecture to
support FL over mobile devices. Besides, as modern machine learning models are
more and more complex, the local on-device training/intermediate model update
in FL is becoming too power hungry/radio resource intensive for mobile devices
to afford. To address those challenges, in this paper, we try to bridge another
recent surging technology, 5G, with FL, and develop a wireless transmission and
weight quantization co-design for energy efficient FL over heterogeneous 5G
mobile devices. Briefly, the 5G featured high data rate helps to relieve the
severe communication concern, and the multi-access edge computing (MEC) in 5G
provides a perfect network architecture to support FL. Under MEC architecture,
we develop flexible weight quantization schemes to facilitate the on-device
local training over heterogeneous 5G mobile devices. Observed the fact that the
energy consumption of local computing is comparable to that of the model
updates via 5G transmissions, we formulate the energy efficient FL problem into
a mixed-integer programming problem to elaborately determine the quantization
strategies and allocate the wireless bandwidth for heterogeneous 5G mobile
devices. The goal is to minimize the overall FL energy consumption (computing +
5G transmissions) over 5G mobile devices while guaranteeing learning
performance and training latency. Generalized Benders' Decomposition is applied
to develop feasible solutions and extensive simulations are conducted to verify
the effectiveness of the proposed scheme.
