  In Federated Learning (FL), a global statistical model is developed by
encouraging mobile users to perform the model training on their local data and
aggregating the output local model parameters in an iterative manner. However,
due to limited energy and computation capability at the mobile devices, the
performance of the model training is always at stake to meet the objective of
local energy minimization. In this regard, Multi-access Edge Computing
(MEC)-enabled FL addresses the tradeoff between the model performance and the
energy consumption of the mobile devices by allowing users to offload a portion
of their local dataset to an edge server for the model training. Since the edge
server has high computation capability, the time consumption of the model
training at the edge server is insignificant. However, the time consumption for
dataset offloading from mobile users to the edge server has a significant
impact on the total time consumption. Thus, resource management in MEC-enabled
FL is challenging, where the objective is to reduce the total time consumption
while saving the energy consumption of the mobile devices. In this paper, we
formulate an energy-aware resource management for MEC-enabled FL in which the
model training loss and the total time consumption are jointly minimized, while
considering the energy limitation of mobile devices. In addition, we recast the
formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture
the coupling constraints between the radio resource management and dataset
offloading. We then analyze the impact of the dataset offloading and computing
resource allocation on the model training loss, time, and the energy
consumption.
