3 papers
cs.LG2025
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
Xianke Qiang, Zheng Chang, Ying-Chang Liang
Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine…
cs.DC2025
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…
cs.LG2024
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…