Intelligent Parameter Transmission in Federated Learning: Managing the trade-off between performance and energy consumption in Wireless IoT Networks

• Renan R. de Oliveira UFG / IFG
• Pedro Augusto S. Belo UFG
• Nickolas Carlos C. Silva UFG
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS

DOI: https://doi.org/10.5753/erigo.2024.4832

Abstract

Federated Learning (FL) allows devices to train a global machine learning model without sharing data, preserving users privacy. FL faces significant challenges related to energy cost, especially in resource-limited environments, such as wireless networks and emerging Internet of Things (IoT) solutions. This work presents an intelligent parameter transmission strategy in the context of FL in wireless IoT networks. The proposed approach is based on the relative percentage difference between the old weights and the new updated weights of the local models, using a defined threshold to decide on the need for transmission. The results demonstrate that conditional transmission reduces the number of transmissions without significantly compromising the accuracy of the global model.

Keywords: Federated Learning, IoT, Intelligent Transmission, Energy Consumption, Wireless Networks

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