Optimising the computational and cost efficiency of hierarchical federated edge learning
chen wang · 2024
Hierarchical Federated Edge Learning (HFEL) is a promising and efficient framework , providing privacy preservation, which aims to address the issue of limited resources and network congestion by utilizing the available resources in the edge network. This paper proposes a new scheme, HFEL-Q, based on HFEL to address the high energy consumption problem of FL training, as well as the inherent communication and user heterogeneity problems. To improve training performance, a utility function is designed based on users' learning quality and training time to efficiently select the user group with the highest utility. Additionally, a frequency determination method is employed to optimize idle time and reduce energy consumption during training. Finally, the performance of HFEL-Q is evaluated on two real datasets to demonstrate its superiority over state-of-the-art baselines in terms of training rate, accuracy, and energy savings.