Improving Energy Efficiency in Federated Learning Through the Optimization of Communication Resources Scheduling of Wireless IoT Networks

TL;DR

This paper introduces FL-E2WS, a novel federated learning algorithm optimized for wireless IoT networks that reduces energy consumption and improves model accuracy by intelligently scheduling communication resources considering channel conditions.

Contribution

The work presents a new FL algorithm that optimizes communication resource scheduling in wireless IoT environments, addressing energy efficiency and model accuracy.

Findings

Reduces energy consumption by up to 70.12%.

Improves global model accuracy by up to 10.21%.

Achieves up to 38.61% energy savings over scaled communication methods.

Abstract

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%.