Publications (13)
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Qiong Wu, Xiaobo Wang, Qiang Fan +3
Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In t…
Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles
Cui Zhang, Maoxin Ji, Qiong Wu +2
As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of…
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Wenhua Wang, Yu Zhao, Qiong Wu +3
Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the co…
First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network
Jianhang Xu, Mohan Chen, Cui Zhang +1
An accurate ab initio theory of the H-bond structure of liquid water requires a high-level exchange correlation approximation from density functional theory. Based on the liquid st…
Single-Step Six-Dimensional Movable Antenna Reconfiguration for High-Mobility IoV: Modeling, Analysis, and Optimization
Maoxin Ji, Qiong Wu, Pingyi Fan +4
The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applyi…
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Cui Zhang, Wenjun Zhang, Qiong Wu +4
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead…