To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …

Multi-access edge computing (MEC) is the key enabling technology that supports compute-
intensive applications in 5G networks. By deploying powerful servers at the edge of wireless …

Although mission-critical applications require the use of deep neural networks (DNNs), their
continuous execution at mobile devices results in a significant increase in energy …

Edge computing, as one of the key technologies in 6G networks, establishes a distributed
computing environment by deploying computation and storage resources in proximity to end …

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning
applications, and have been widely used on mobile devices. Running DNNs on …

Abstract Distributed training of Machine Learning models in edge Internet of Things (IoT)
environments is challenging because of three main points. First, resource-constrained …

Sixth generation mobile networks (6G) may experience a huge evolution on vertical industry
scenarios, where deep edge networks () become an important network structure for the …

5G and beyond cellular networks (NextG) will support the continuous execution of resource-
expensive edge-assisted deep learning (DL) tasks. To this end, Radio Access Network …

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed
as an enhancement of federated learning model. Such a system generally consists of three …

5G and edge computing will serve various emerging use cases that have diverse
requirements of multiple resources, eg, radio, transportation, and computing. Network slicing …