Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective

TK Rodrigues, K Suto, H Nishiyama… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) is considered an essential future service for the
implementation of 5G networks and the Internet of Things, as it is the best method of …

Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges

G Bao, P Guo - Journal of Cloud Computing, 2022 - Springer
In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative
architecture has been proposed to compensate for the lack of data processing power of …

Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles

Y Lu, X Huang, K Zhang, S Maharjan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can
improve the driving experience and service quality. However, the bandwidth, security and …

Reducing offloading latency for digital twin edge networks in 6G

W Sun, H Zhang, R Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
6G is envisioned to empower wireless communication and computation through the
digitalization and connectivity of everything, by establishing a digital representation of the …

Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization

M Li, N Cheng, J Gao, Y Wang, L Zhao… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing
(MEC) with the objective to optimize computation offloading with minimum UAV energy …

A survey of federated learning for edge computing: Research problems and solutions

Q Xia, W Ye, Z Tao, J Wu, Q Li - High-Confidence Computing, 2021 - Elsevier
Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Task offloading in vehicular edge computing networks: A load-balancing solution

J Zhang, H Guo, J Liu, Y Zhang - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-
sensitive vehicular applications such as automatic driving, auto navigation. Note that …

Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond

Y Dai, K Zhang, S Maharjan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …

Partial offloading scheduling and power allocation for mobile edge computing systems

Z Kuang, L Li, J Gao, L Zhao… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
Mobile edge computing (MEC) is a promising technique to enhance computation capacity at
the edge of mobile networks. The joint problem of partial offloading decision, offloading …

Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks

Y Dai, D Xu, K Zhang, S Maharjan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data
and multimedia content to be cached in proximity to vehicles. However, high mobility of …