  We investigate resource allocation scheme to reduce the energy consumption of
federated learning (FL) in the integrated fog-cloud computing enabled
Internet-of-things (IoT) networks. In the envisioned system, IoT devices are
connected with the centralized cloud server (CS) via multiple fog access points
(F-APs). We consider two different scenarios for training the local models. In
the first scenario, local models are trained at the IoT devices and the F-APs
upload the local model parameters to the CS. In the second scenario, local
models are trained at the F-APs based on the collected data from the IoT
devices and the F-APs collaborate with the CS for updating the model
parameters. Our objective is to minimize the overall energy-consumption of both
scenarios subject to FL time constraint. Towards this goal, we devise a joint
optimization of scheduling of IoT devices with the F-APs, transmit power
allocation, computation frequency allocation at the devices and F-APs and
decouple it into two subproblems. In the first subproblem, we optimize the IoT
device scheduling and power allocation, while in the second subproblem, we
optimize the computation frequency allocation. For each scenario, we develop a
conflict graph based solution to iteratively solve the two subproblems.
Simulation results show that the proposed two schemes achieve a considerable
performance gain in terms of the energy consumption minimization. The presented
simulation results interestingly reveal that for a large number of IoT devices
and large data sizes, it is more energy efficient to train the local models at
the IoT devices instead of the F-APs.
