Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network
to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A …

The computational capabilities of mobile devices have been advancing at a rapid pace in
recent times, leading to a growing interest in deploying machine learning applications on …

Edge intelligence is an emerging paradigm for real-time training and inference at the
wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) …

The demand for intelligent services at the network edge has introduced several research
challenges. One is the need for a machine learning architecture that achieves …

With the wave of the Internet of Things (IoT), a vast number of IoT devices are connected to
wireless networks. To better support the Quality of Service of IoT devices with constrained …

With the increased penetration and proliferation of Internet of Things (IoT) devices, there is a
growing trend towards distributing the power of deep learning (DL) across edge devices …

Recent years have witnessed deep neural networks (DNNs) become the de facto tool in
many applications such as image classification and speech recognition. But significant …

Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural
Network (DNN) models in proximity to the sources of data. To enable the training of large …

In this paper, we study fine-grained offloading for multi-access edge computing (MEC) in 5G.
Existing works for computation offloading is on a per-task basis and do not take into account …

Deep learning has emerged as a powerful technique for processing and extracting insights
from complex data. However, the resource-constrained nature of edge devices poses …