1 code implementation • 1 Dec 2023 • Xingqiu He, Chaoqun You, Tony Q. S. Quek
In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used.
no code implementations • 23 Mar 2023 • Chaoqun You, Kun Guo, Gang Feng, Peng Yang, Tony Q. S. Quek
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
no code implementations • 19 Mar 2023 • Chaoqun You, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
no code implementations • 27 Sep 2022 • Chaoqun You, Daquan Feng, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
no code implementations • 3 Jun 2021 • Peng Yang, Tony Q. S. Quek, Jingxuan Chen, Chaoqun You, Xianbin Cao
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users.