418 citations · 484 across the 5 of their papers we have counts for
5 papers
Federated Ensemble Model-based Reinforcement Learning in Edge Computing
Jin Wang, Jia Hu, Jed Mills +2
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices…
Accelerating Federated Learning with a Global Biased Optimiser
Jed Mills, Jia Hu, Geyong Min +3
Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data pri…
Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach
Jin Wang, Jia Hu, Geyong Min +2
Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. A…
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Jin Wang, Jia Hu, Geyong Min +2
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently…
DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks
Abhijeet Bhorkar, Ke Zhang, Jin Wang
Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate predi…