  Federated Learning (FL) allows devices to train a global machine learning
model without sharing data. In the context of wireless networks, the inherently
unreliable nature of the transmission channel introduces delays and errors that
compromise the regularity of updating the global model. Furthermore, limited
resources and energy consumption of devices are factors that affect FL
performance. Therefore, this work proposes a new FL algorithm called FL-E2WS
that considers both the requirements of federated training and a wireless
network within the scope of the Internet of Things. To reduce the energy cost
of devices, FL-E2WS schedules communication resources to allocate the ideal
bandwidth and power for the transmission of models under certain device
selection and uplink resource block allocation, meeting delay requirements,
power consumption, and packet error rate. The simulation results demonstrate
that FL-E2WS reduces energy consumption by up to 70.12% and enhances the
accuracy of the global model by up to 10.21% compared to the FL algorithms that
lacks transmission channel knowledge. Additionally, when compared to FL
versions that scale communication resources, FL-E2WS achieves up to a 38.61%
reduction in energy consumption and improves the accuracy of the global model
by up to 1.61%.
