P Tam, S Math, A Lee, S Kim - Computers, Materials & Continua, 2022 - researchgate.net
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 …
T Subramanya, R Riggio - IEEE Transactions on Network and …, 2021 - ieeexplore.ieee.org
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 …
K Zhan, N Chen, SVN Santhosh Kumar… - International Journal …, 2022 - Wiley Online Library
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 …
Y Gong, H Yao, J Wang, D Wu, N Zhang, FR Yu - IEEE Network, 2022 - ieeexplore.ieee.org
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 …
Q Zhang, L Gui, S Zhu, X Lang - IEEE Access, 2021 - ieeexplore.ieee.org
Computation-intensive mobile applications are explosively increasing and cause computation overload for smart mobile devices (SMDs). With the assistance of mobile edge …
P Tam, S Kang, S Ros, S Kim - Electronics, 2023 - mdpi.com
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 …
J Son, R Buyya - Journal of Systems and Software, 2019 - Elsevier
Abstract The emergence of Network Function Virtualization (NFV) enabled decoupling network functionality from dedicated hardware and placing them upon generic computing …
J Liu, H Xu, L Wang, Y Xu, C Qian… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
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 …