Federated Split Learning for Edge Intelligence in Resource-Constrained Wireless Networks

The rapid advancement of the Internet of Things (IoT) and artificial intelligence has significantly increased the number of consumer electronics devices. Traditional federated learning methods typically require all these devices to collaboratively train deep neural networks (DNNs). However, this approach poses a significant challenge due to the substantial communication overhead incurred by uploading and downloading the large DNN models between the base station and resource-constrained IoT devices. To overcome this challenge, we propose a federated split learning (FedSL) framework, along with two variants (FedSL-I and FedSL-II). Furthermore, considering the limitations posed by battery capacity, heterogeneous channels and computing capabilities across all consumer devices, we jointly optimize power allocation, device scheduling and split layer selection to minimize the per-round weighted sum of training delay and energy consumption. We design alternating optimization algorithms to attain a sub-optimal solution with reduced computational complexity. Simulation results demonstrate that our proposed FedSL enables efficient collaborative model training among IoT devices, leading to a significant reduction in communication overhead, and ultimately enhancing the overall performance.