Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round
communications of multi-dimensional model updates between local participants and the …

Federated learning (FL) activates distributed on-device computation techniques to model a
better algorithm performance with the interaction of local model updates and global model …

Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two
technologies expected to play a vital role in 5G and beyond networks. However, adequate …

It is expected that the demand for quality of service (QoS) and quality of experience (QoE) in
future 6G scenarios will continue to increase, and edge computing (EC) will continue to …

With the development of artificial intelligence of things (AIoT), multi-access edge computing
(MEC) becomes a key enabler to migrate cloud services to edge clients. In comparison to …

Computation-intensive mobile applications are explosively increasing and cause
computation overload for smart mobile devices (SMDs). With the assistance of mobile edge …

The advancement of the sensing capabilities of end devices drives a variety of data-
intensive insights, yielding valuable information for modelling intelligent industrial …

Deep-learning-based intelligent services have become prevalent in cyber-physical
applications, including smart cities and health-care. Deploying deep-learning-based …

Abstract The emergence of Network Function Virtualization (NFV) enabled decoupling
network functionality from dedicated hardware and placing them upon generic computing …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …