24 citations · 27 across the 5 of their papers we have counts for
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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…
cs.LG2024
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…
cs.DC2024★ 24 cited
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…