Jun Zhang
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Associate Professor, IEEE Fellow Distinguished Lecturer, IEEE Communications Society Office: Room 2430 |
What's new
(Student Awards) Congratulations to Xinran, Zhening, Jiawei, and Teng!
(August 2023) Xinran received The ECE Best TA Award.
(August 2023) Zhening received the HKUST RedBird PhD Award.
(June 2023) Jiawei received the HKUST RedBird Academic Excellence Award for Continuing PhD Students.
(April 2023) Teng received the Hong Kong PhD Fellowship.
(HK 6G Wireless Summit) The IEEE Hong Kong 6G Wireless Summit (IEEE HK6GWS 2023), 13-14 September 2023.
(Call for Paper) “Digital Twins Meet Artificial Intelligence in 6G”, a feature topic on IEEE Communications Magazine. (Deadline: 31 March 2024)
(New Survey) “A survey of what to share in federated learning: Perspectives on model utility, privacy leakage, and communication efficiency,” submitted. [Paper]
(New Paper) “Large language models empowered autonomous edge AI for connected intelligence,” submitted. [Paper]
(New Paper) “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” submitted. [Paper]
(CVPR 23) “Generalized relation modeling for transformer tracking”, accepted by CVPR 2023. [Paper] [GitHub]
(ICLR 23) Two papers accepted to ICLR 2023.
(AAMAS 23) “AC2C: Adaptively controlled two-hop communication for multi-agent reinforcement learning,” accepted by AAMAS 2023. [Paper]
Research Interests
Edge AI and Edge Computing
Edge video analytics; deep image/video compression; cooperative perception
Cooperative AI
Privacy-preserving collaborative learning; multi-agent reinforcement learning
Wireless Communications and Networking
Machine learning for wireless communications; wireless sensing
Selected Publications
Edge AI and Edge Computing
X. Zhang, J. Shao, and J. Zhang, “Low-complexity deep video compression with a distributed coding architecture,” IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia, July 2023. [Paper] [GitHub]
S. Gao, C. Zhou, and J. Zhang, “Generalized relation modeling for transformer tracking,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023. [Paper] [GitHub]
X. Zhang, J. Shao, and J. Zhang, “LDMIC: Learning-based distributed multi-view image coding,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. [Paper] [GitHub]
J. Shao, X. Zhang, and J. Zhang, “Task-oriented communication for edge video analytics,” IEEE Trans. Wireless Commun., to appear. [Paper] [GitHub]
J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, vol. 40, no. 1, pp. 197-211, Jan. 2022. [Paper] [GitHub]
Federated Learning, Safe/Robust AI
Z. Li, Y. Sun, J. Shao, Y. Mao, J. Wang, and J. Zhang, “Feature matching data synthesis for non-IID federated learning,” submitted. [Paper]
J. Shao, F. Wu, and J. Zhang, “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” submitted. [Paper]
B. Li*, Y. Shen*, J. Yang, Y. Wang, J. Ren, T. Che, J. Zhang, and Z. Liu, “Sparse Mixture-of-Experts are Domain Generalizable Learners,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (Oral presentation) [Paper] [GitHub]
T. Zhou, J. Zhang, and D. Tsang, “FedFA: Federated learning with feature anchors to align feature and classifier for heterogeneous data,” submitted. [Paper]
J. Shao, Y. Sun, S. Li, and J. Zhang, “DReS-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,” NeurIPS 2022. [Paper] (Acceptance Rate: 25.6%)
Wireless Communications
W. Yu, Y. Shen, H. He, X. Yu, S.H. Song, J. Zhang, and K. B. Letaief, “An adaptive and robust deep learning framework for THz ultra-massive MIMO channel estimation,” IEEE J. Sel. Topics Signal Process., to appear. [Paper] [GitHub]
Y. Shen, J. Zhang, S.H. Song, and K. B. Letaief, “Graph neural networks for wireless communications: From theory to practice,” IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 3554-3569, May 2023. [Paper] [GitHub]
Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun, vol. 39, no. 1, pp. 101–115, Jan. 2021. [Paper]