LAUSR

Nowadays, federated learning (F1) has been proposed as an emerging technology to store sensory data and train the edge networks using a set of computing devices with minimum training time. However, a collection of heterogeneous participating devices with different processing power and energy usage are used to analyze the model parameters locally. Therefore, an intelligent service provisioning mechanism with the F1 technique needs to be developed at the edge networks. This strategy can increase the security and privacy of the network while minimizing the training time on resource-constrained edge devices. In this magazine, we describe the importance of the FL-aided hybrid edge intelligent framework for next-generation Internet of Things applications. Moreover, to enhance the critical service provisioning functionality, we highlight two use case studies along with their potential research directions, including intelligent transportation systems and intelligent healthcare systems. Finally, this work concludes with a set of potential future research directions of FL-aided edge networks.