Guest editorial of the special section on split federated learning for resource-constrained consumer electronics: Applications and challenges

Next-generation wireless networks are poised to redefine our digital world, by integrating advanced artificial intelligence (AI) capabilities, enabling intelligent support for emerging applications such as Extended Reality (XR), Holographic Telepresence, Robotics, and Autonomous Mobility. In this environment, Consumer Electronics (CE) devices, such as smartphones, smartwatches, medical implants, and IoT devices, will play a crucial role in various real-world scenarios. However, due to their limited battery and processing capacities; training complex and large deep learning models on such devices is computationally intensive and can result in high energy and power consumption, and early termination of the training. Therefore, it is crucial to optimize the training process using lightweight models and offloading computation to more powerful servers while providing good learning performance. Distributed machine/deep learning algorithms offer viable alternatives in this context. They consist of deploying distributed agents that collaborate to train complex models without sharing local data. Federated Learning (FL) is a popular distributed learning paradigm in the literature. Nevertheless, as mentioned earlier, training the full FL model on computationally constrained nodes is not feasible.