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

• Edge LAM
  • Our extended research on Edge LAM within the framework of IoT systems, titled “Edge Large AI Models: Collaborative Deployment and IoT Applications,” accepted for publication in IEEE IoT. Mag...
  • Our blue picture in Edge LAM, “Edge Large AI Models: Revolutionizing 6G Networks,” accepted for publication in IEEE Commun. Mag..
  • Our first work in edge LAM, “Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks”, accepted for publication in IEEE Trans. Wireless Commun..
  • By digging deep into the communication and computational logic of LoRA, our work “Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks” accepted for publication in MEDITCOM 2025. This paper further explores the FedFT framework in conjunction with differential privacy and proposes a FedFT with DP framework, which investigates the impact of noise on FedFT with LoRA. The simulation results are superior to the ICLR2024 paper.

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)