116 citations · 231 across the 8 of their papers we have counts for
9 papers
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Xiaohuan Li, Junchuan Fan, Bingqi Zhang +3
To implement the intelligent transportation digital twin (ITDT), unmanned aerial vehicles (UAVs) are scheduled to process the sensing data from the roadside sensors. At this time,…
Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach
Xumin Huang, Yuan Wu, Jiawen Kang +4
Metaverse enables users to communicate, collaborate and socialize with each other through their digital avatars. Due to the spatio-temporal characteristics, co-located users are se…
Federated Learning-Empowered AI-Generated Content in Wireless Networks
Xumin Huang, Peichun Li, Hongyang Du +4
Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process…
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses
Jiawen Kang, Jiayi He, Hongyang Du +4
For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (Se…
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li, Guoliang Cheng, Xumin Huang +4
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjus…
Joint User Association and Resource Pricing for Metaverse: Distributed and Centralized Approaches
Xumin Huang, Weifeng Zhong, Jiangtian Nie +4
Metaverse as the next-generation Internet provides users with physical-virtual world interactions. To improve the quality of immersive experience, users access to Metaverse service…
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing
Peichun Li, Xumin Huang, Miao Pan +1
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied…
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
Xumin Huang, Peichun Li, Rong Yu +3
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the applicatio…
Permissioned Blockchain for Efficient and Secure Resource Sharing in Vehicular Edge Computing
Siming Wang, Xumin Huang, Rong Yu +2
With the fast expanding scale of vehicular networks, vehicular edge computing (VEC) has emerged and attracted growing attention from both industry and academia. Parked vehicles (PV…