24 citations · 27 across the 5 of their papers we have counts for
5 papers
Semantic-aware Token Selection and Resource Optimization for Communication-efficient Split Federated Fine-tuning in Edge Intelligence
Xianke Qiang, Zheng Chang, Geyong Min
Deploying large Transformer-based vision models on resource-limited mobile devices at network edge is severely constrained by hardware limitations and dynamic wireless environments…
AIGC-assisted Federated Learning for Vehicular Edge Intelligence: Vehicle Selection, Resource Allocation and Model Augmentation
Xianke Qiang, Zheng Chang, Geyong Min
To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehic…
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
Lu Yu, Zheng Chang, Yunjian Jia +1
The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocatio…
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
Luying Zhong, Yueyang Pi, Zheyi Chen +4
Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most exis…
Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design and Future Directions
Xianke Qiang, Zheng Chang, Chaoxiong Ye +2
To achieve ubiquitous intelligence in future vehicular networks, artificial intelligence (AI) is essential for extracting valuable insights from vehicular data to enhance AI-driven…