Hi, I’m Zixin Wang (王子鑫)

Currently, I’m a post-doctoral fellow in the Dept. ECE, Hong Kong University of Science and Technology (HKUST), working with Prof. Khaled B. Letaief.

Prior to this, I have received the Ph.D. degree from University of Chinese Academy of Sciences (ShanghaiTech University), Shanghai, China, in 2024, co-supervised by Prof. Yong Zhou and Prof. Yuanming Shi, and the B.Sc. degree from Wuhan University of Technology, Wuhan, China, in 2018. During Nov. 2022 to Oct. 2023, I was a visiting doctoral researcher in CWC, Oulu University, supervised by Prof. Mehdi Bennis.

My research areas include edge intelligence, edge large AI model, federated learning, and network optimization.

News

  • May 2025, our work “Edge Large AI Models: Collaborative Deployment and IoT Applications” is accepted by IEEE IoT. Mag.! On the same day!
  • May 2025, our work “Edge Large AI Models: Revolutionizing 6G Networks” is accepted by IEEE Commun. Mag.!
  • Mar. 2025, our work “Microservice Migration in Hybrid Satellite-Terrestrial Networks for Autonomous Vehicles” is accepted by J. Commun. Info. Netw. and selected for the cover article.
  • Mar. 2025, our work “Learning to Beamform for Integrated Sensing and Communication: A Graph Neural Network with Implicit Projection Approach” is accepted by IEEE Trans. Wireless Commun. [Paper link].
  • Jan. 2025, our work “Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks” is accepted by IEEE Trans. Wireless Commun.. [Paper link]
  • Dec. 2024, I had a wonderful experience at GLOBECOM 2024, Capetown, SA, exchanging ideas about FedFT and learning more from others.
  • Sept. 2024, it is glad to attend IEEE Hong Kong 6G Wireless Summit.
  • Sept. 2024, our work “Over-the-air Federated Graph Learning” is accepted by IEEE Trans. Wireless Commun..
  • Aug. 2024, our work “Graph Attention-based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems” is accepted by IEEE Trans. Wireless Commun..
  • Aug. 2024, our work “Federated Low-Rank Adaptation for Large Language Model Fine-Tuning Over Wireless Networks” is accepted by GLOBECOM 2024.
  • Mar. 2024, I joined HKUST as a post-doctoral fellow, working with Prof. Khaled B. Letaief.
  • Nov. 2023, I passed my thesis defense.
  • Nov. 2022, I start my visiting @Oulu University as a visiting doctoral researcher, working with Prof. Mehdi Bennis.

Highlighted Ongoing Work

Selected Papers by Area

Edge Large AI Model

  • Z. Wang, Yuanming Shi, and Khaled. B. Letaief. ‘‘Edge Large AI Models: Collaborative Deployment and IoT Applications,’’ accepted by IEEE IoT Mag., 2025, to appear.
  • Z. Wang, Y Shi, Y Zhou, J Zhu, K Letaief. ‘‘Edge Large AI Models: Revolutionizing 6G Networks,’’ accepted by IEEE Commun. Mag., 2025, to appear.
  • Z. Wang, Y. Zhou, Y. Shi, and K. B. Letaief. ‘‘Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks’’, accepted for publication in IEEE Trans. Wireless Commun., 2025.
  • T. Kang, Z. Wang, H. He, J. Zhang, Jun, S. Song, and K. B. Letaief. ‘‘Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks’’, accepted for publication in MeditCom 2025.

Federated Learning

  • Z. Wang, Y. Zhou, and Y. Shi. ‘‘Over-the-air Federated Graph Learning’’, accepted for publication inIEEE Trans. Wireless Commun., 2024.
  • Z. Wang, Y. Zou, Q. An, Y. Zhou, Y. Shi, and M. Bennis. ‘‘A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning’’, accepted for publication in IEEE Trans. Wireless Commun., 2024.
  • G Gao, Q An, Z Wang, Z. Wang, Y Shi, Y Zhou. ‘‘Over-the-air computation assisted federated learning with progressive training,’’ accepted for publication in ICC 2024.
  • Y Zou, Z Wang, X Chen, H Zhou, Y Zhou. ‘‘Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning’’, accepted for publication in IEEE Trans. Wireless Commun., 2023.
  • Z Yang, Y Shi, Y Zhou, Z. Wang, K Yang. ‘‘Trustworthy federated learning via blockchain,’’ accepted for publication in IEEE IoT. J., 2022.

Integrated Sensing And Communication

  • Y. Zhao, Y. Zhou, Z. Wang, Y. Shi; N. Cheng, and Haibo Zhou. ‘‘Learning to Beamform for Integrated Sensing and Communication: A Graph Neural Network with Implicit Projection Approach,’’ accepted for publication in IEEE Trans. Wireless Commun., 2025.

AI-enabled Network Optimization

  • S Wan, Z. Wang, Y Zhou. ‘‘Scalable Hybrid Beamforming for Multi-User MISO Systems: A Graph Neural Network Approach,’’ accepted for publication in IEEE Trans. Wireless Commun., 2024.
  • Z. Wang, M. Bennis, and Y. Zhou. ‘‘Graph Attention-based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems’’, accepted for publication in IEEE Trans. Wireless Commun., 2024.
  • Z. Wang, J. Zong, Y. Zhou, Y. Shi, and V. W.S. Wong. ‘‘Decentralized Multi-Agent Power Control in Wireless Networks with Frequency Reuse’’ accepted for publication in IEEE Trans. Commun., 2021.

我见青山多妩媚, 料青山见我应如是。—— 辛弃疾 (1140–1207)