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Publications

  • He, H., Gu, Y., Hu, Y., Fang, F., Ning, X., Chen, X., Cheng, L. (2025). Real-time workflow scheduling in hybrid clouds with privacy and security constraints: A deep reinforcement learning approach. Expert Systems with Applications, 278, 127376. https://doi.org/10.1016/j.eswa.2025.127376 [Deposited version]

  • Yu, L., Chang, Z., Jia, Y., Min, G. (2025). Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2025.3554247

  • Wan, F., Wang, K., Wang, T., Qin, H., Fondrevelle, J., Duclos, A. (2025). Enhancing healthcare resource allocation through large language models. Swarm and Evolutionary Computation, 94. https://doi.org/10.1016/j.swevo.2025.101859

  • Huang, T., Liu, J., Chang, Z., Wei, Y., Zhao, X., Liang, Y.-C. (2025). Energy Efficient Spectrum Sharing and Resource Allocation for 6G Air-Ground Integrated Networks. IEEE Transactions on Network and Service Management. https://doi.org/10.1109/TNSM.2025.3527651

  • Athema, A., Wang, K., Chen, X., Li, Y. (2024). Semantic Communications for Healthcare Applications: Opportunities and Challenges. 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). https://doi.org/10.1109/bdcat63179.2024.00066

  • Wan, F., Fondrevelle, J., Wang, T., Wang, K., Duclos, A. (2024). Optimizing Small-Scale Surgery Scheduling with Large Language Model. Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics. https://doi.org/10.5220/0012894400003822

  • Zhu, R., Yang, M., Wang, Q. (2024). ShuffleFL: Addressing Heterogeneity in Multi-Device Federated Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(2), Article 85, 34 pages. https://doi.org/10.1145/3659621

  • Liu, J., Chang, Z., Ye, C., Mumtaz, S., Hämäläinen, T. (2024). Game-Theoretic Power Allocation and Client Selection for Privacy-Preserving Federated Learning in IoMT. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2024.3523968

  • Yang, M., Zhu, R., Wang, Q., Yang, J. (2024). FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning. ICLR. https://openreview.net/forum?id=8IsP38Hpq2