Split Federated Learning-Empowered Energy-Efficient Mobile Traffic Prediction Over UAVs

Faranaksadat Solat, Joohyung Lee, Dusit Niyato

Abstract: In this letter, to alleviate the training burden over Unmanned Aerial Vehicles (UAVs) for generating the mobile traffic prediction model collaboratively, we design a novel energy-efficient mobile traffic prediction framework empowered by Split Federated Learning (SFL) for UAV networks, termed E-SFL. For this purpose, we rigorously formulated an analytical model of the overall energy consumption of UAVs, including both computing and networking energy consumption. The experimental results for two real-world mobile traffic datasets show that the proposed E-SFL surpasses previous state-of-the-art methods in terms of energy consumption with an acceptable accuracy loss.