| Pushing large language models to the 6g edge: Vision, challenges, and opportunities Z Lin, G Qu, Q Chen, X Chen, Z Chen, K Huang arXiv preprint arXiv:2309.16739, 2023 | 85 | 2023 |
| Split learning in 6g edge networks Z Lin, G Qu, X Chen, K Huang IEEE Wireless Communications, 2024 | 62 | 2024 |
| Vehicle as a service (VaaS): Leverage vehicles to build service networks and capabilities for smart cities X Chen, Y Deng, H Ding, G Qu, H Zhang, P Li, Y Fang IEEE Communications Surveys & Tutorials, 2024 | 40 | 2024 |
| Adaptsfl: Adaptive split federated learning in resource-constrained edge networks Z Lin, G Qu, W Wei, X Chen, KK Leung arXiv preprint arXiv:2403.13101, 2024 | 36 | 2024 |
| Mobile edge intelligence for large language models: A contemporary survey G Qu, Q Chen, W Wei, Z Lin, X Chen, K Huang IEEE Communications Surveys & Tutorials, 2025 | 29 | 2025 |
| Optimal resource allocation for u-shaped parallel split learning S Lyu, Z Lin, G Qu, X Chen, X Huang, P Li 2023 IEEE Globecom Workshops (GC Wkshps), 197-202, 2023 | 16 | 2023 |
| TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks G Qu, Z Lin, F Liu, X Chen, K Huang arXiv preprint arXiv:2405.03990, 2024 | 14 | 2024 |
| TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading G Qu, Z Lin, Q Chen, J Li, F Liu, X Chen, K Huang arXiv preprint arXiv:2404.14204, 2024 | 8 | 2024 |
