Energy-Efficient Federated Learning in Symbiotic IoT Networks through Heterogeneity-aware Client Sampling

Federated Learning (FL) in symbiotic IoT networks is a promising collaborative paradigm that utilizes IoT devices to co-train machine learning models, promising to accelerate edge intelligence for 6G. Existing studies on heterogeneous FL in IoT networks focus mainly on the differences in link capacity, ignoring the fundamental impact of channel fluctuation on model transmission and communication energy consumption. FL in symbiotic IoT networks still faces the challenges of heterogeneous and dynamic wireless links and inter-round competition of limited resource allocation, significantly impacting energy efficiency and learning performance. To address this issue, we first model wireless channel fading and dynamics for FL over symbiotic IoT networks and develop a joint optimization model for energy efficiency and learning performance. Then, we propose a novel heterogeneous-aware client sampling scheme to achieve energy-efficient training by exploiting prompt channel state tracking to predict energy consumption and update the deviation of the energy budget of each client promptly to select the optimal set of clients for each training phase. Finally, extensive experiments show that our proposed client sampling scheme significantly outperforms the existing methods and improves energy efficiency by up to 1.6×.