Mobile Edge Computing (MEC) has emerged as a promising computing paradigm in 5G
networks, which can empower User Equipments (UEs) with computation and energy …

Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user
traffic periods has been proved to be an effective energy saving strategy. However, this …

To enable machine learning at the edge of wireless networks (such as edge cloud), close to
mobile users, is critical for future wireless networks, but challenging since the lower layers in …

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services
for mobile devices by leveraging computation and storage resources at the network edge …

Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI
applications. Conventional approaches for training DNNs are generally implemented at …

We consider an Internet of Things (IoT) network, where a large amount of sensor data is
transmitted from wireless IoT devices to a central server for the classification of system …

Mobile edge computing (MEC) emerges as a promising paradigm that extends the cloud
computing to the edge of pervasive radio access networks, in near vicinity to mobile users …

Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …