41 citations · 102 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…