41 citations · 102 across the 10 of their papers we have counts for
10 papers
Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?
Kun Guo, Yun Shen, Xijun Wang +3
Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent adv…
FlexSAN: A Flexible Regenerative Satellite Access Network Architecture
Weize Kong, Chaoqun You, Xuming Pei +1
The regenerative satellite access network (SAN) architecture deploys next-generation NodeB (gNBs) on satellites to enable enhanced network management capabilities. It supports two…
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
Shuai Wang, Yanqing Xu, Chaoqun You +2
Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hin…
Exploiting Storage for Computing: Computation Reuse in Collaborative Edge Computing
Xingqiu He, Chaoqun You, Tony Q. S. Quek
Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance…
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Xingqiu He, Chaoqun You, Tony Q. S. Quek
With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications…
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
Chaoqun You, Kun Guo, Gang Feng +2
Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a Model-Agnostic Meta…
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Chaoqun You, Kun Guo, Howard H. Yang +1
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in m…
Social Welfare Maximization for Collaborative Edge Computing: A Deep Reinforcement Learning-Based Approach
Xingqiu He, Yuhang Shen, Hongxi Zhu +3
Collaborative Edge Computing (CEC) is an effective method that improves the performance of Mobile Edge Computing (MEC) systems by offloading computation tasks from busy edge server…
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Chaoqun You, Daquan Feng, Kun Guo +2
Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). H…
Hierarchical Multi-resource Fair Queueing for Packet Processing
C. You, Y. Zhao, G. Feng +2
Various middleboxes are ubiquitously deployed in networks to perform packet processing functions, such as firewalling, proxy, scheduling, etc., for the flows passing through them.…