24 citations · 28 across the 7 of their papers we have counts for
4 papers · 1 filter
Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study
Lu Yu, Zheng Chang, Ying-Chang Liang
Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems. However, traditional centralized learning in dynamic vehicular networks faces significant…
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
Xianke Qiang, Hongda Liu, Xinran Zhang +2
Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations a…
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…
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…