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Zehong Lin

Research Assistant Professor

Department of Electronic and Computer Engineering (ECE)
The Hong Kong University of Science and Technology (HKUST)

Address: Room 3112A, Academic Building, HKUST
Email: eezhlin@ust.hk
[Google Scholar] [GitHub]

About Me

I am currently a Research Assistant Professor with the Department of Electronic and Computer Engineering at The Hong Kong University of Science and Technology. I received the Ph.D. degree in Information Engineering from The Chinese University of Hong Kong (CUHK) in 2022, and the B.Eng. degree in Information Engineering from South China University of Technology (SCUT) in 2017.

Research Interests

  • Wireless Communications and Networking

  • Federated and Distributed Learning

  • Mobile Edge Computing and Edge AI

  • 5G and Beyond Systems

News

  • New [03/2024] Paper “FedCiR: Client-invariant representation learning for federated non-IID features” was accepted by IEEE Transactions on Mobile Computing (TMC). [Paper]

  • New [03/2024] We will organize the 2nd IEEE Hong Kong 6G Wireless Summit (IEEE HK6GWS 2024) on 11-12 September 2024.

  • New [01/2024] Paper “Spatial-aware latent initialization for controllable image generation” was submitted. [Paper]

  • New [12/2023] Paper “Large language models empowered autonomous edge AI for connected intelligence” was accepted by IEEE Communications Magazine. [Paper]

  • New [11/2023] Paper “Channel and gradient-importance aware device scheduling for over-the-air federated learning” was accepted by IEEE Transactions on Wireless Communications (TWC). [Paper]

  • New [10/2023] Paper “Understanding and improving model averaging in federated learning on heterogeneous data” was submitted. [Paper]

  • New [09/2023] Paper “Probabilistic scheduling for over-the-air federated learning,” was accepted by the 2023 IEEE 23rd International Conference on Communication Technology (ICCT). [Paper]

  • New [08/2023] We will organize the 1st IEEE Hong Kong 6G Wireless Summit (IEEE HK6GWS 2023) on 13-14 September 2023.

  • New [08/2023] Paper “FedCiR: Client-invariant representation learning for federated non-IID features” was submitted. [Paper]

  • New [07/2023] Paper “A survey of what to share in federated learning: Perspectives on model utility, privacy leakage, and communication efficiency” was submitted. [Paper]

  • [10/2022] I joined the Department of ECE at HKUST.

  • [08/2022] I passed my Ph.D. thesis defense.