418 citations · 553 across the 13 of their papers we have counts for
14 papers
Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems
Yi Chen, Zheng Chang, Geyong Min +2
IoT devices recently are utilized to detect the state transition in the surrounding environment and then transmit the status updates to the base station for future system operation…
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…
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Chenyuan Feng, Howard H. Yang, Deshun Hu +3
Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the l…
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…
Evolutionary Multi-Objective Virtual Network Function Placement: A Formal Model and Effective Algorithms
Joseph Billingsley, Ke Li, Geyong Min +1
Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperati…
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…