Dr. Zheng Lin | Edge Intelligence | Young Scientist Award
Dr. Zheng Lin | Edge Intelligence | The University of Hong Kong | Hong Kong
Dr. Zheng Lin is an emerging researcher whose work reflects strong academic depth, interdisciplinary capability, and high-impact scientific potential in the fields of wireless networking, edge intelligence, and distributed edge learning. Dr. Zheng Lin completed his Ph.D. studies in Electrical Engineering at The University of Hong Kong under distinguished supervision, building upon earlier master’s-level training at Fudan University and Fuzhou University, where he developed foundational expertise in wireless communication systems and intelligent edge architectures. His professional experience is strengthened by active participation in international collaborative projects involving satellite-assisted learning, robust federated learning, edge intelligence for large-scale systems, and cognitive spectrum sensing, contributing to both theoretical advancements and system-level implementations. Dr. Zheng Lin’s research interests span wireless networking, edge intelligence, distributed edge learning, split learning, mobile edge computing, and satellite–terrestrial fusion networks, positioning him at the intersection of AI-driven communication systems and next-generation intelligent networks. His research skills include distributed optimization, deep learning model design, communication-efficient learning frameworks, satellite networking, multi-agent collaboration, privacy-preserving intelligence, and performance evaluation of large-scale networked systems. He demonstrates technical versatility through significant contributions to high-quality IEEE and Scopus-indexed publications, including papers accepted in IEEE Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Cognitive Communications and Networking, and IEEE Communications Surveys & Tutorials, as well as conference contributions in LNCS series. These outputs reflect both originality and continuity in scholarly productivity.
Professional Profiles: ORCID
Selected Publications
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Lin, Z., Qu, G., Wei, W., Chen, X., & Leung, K. K. (2025). AdaptSFL: Adaptive Split Federated Learning in Resource-Constrained Edge Networks. IEEE Transactions on Networking.
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Lin, Z., Zhang, Y., Chen, Z., Fang, Z., Yang, Y., Zhang, G., Wu, C., Chen, X., & Gao, Y. (2025). ESL-LEO: An Efficient Split Learning Framework over LEO Satellite Networks. Lecture Notes in Computer Science.
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Qiu, Y., Chen, H., Dong, X., Lin, Z., Liao, I. Y., & Tistarelli, M. (2025). IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer. IEEE Transactions on Information Forensics and Security.
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Lin, Z., Zhang, Y., Chen, Z., Fang, Z., Wu, C., Chen, X., Gao, Y., & Luo, J. (2025). LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks. IEEE Transactions on Mobile Computing.
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Qu, G., Chen, Q., Wei, W., Lin, Z., Chen, X., & Huang, K. (2025). Mobile Edge Intelligence for Large Language Models: A Contemporary Survey. IEEE Communications Surveys & Tutorials.
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Zhang, Y., Chen, Z., Hu, X., Zhao, J., & Gao, Y., with contribution from Lin, Z. (2025). S-Leon: An Efficient Split Learning Framework Over Heterogeneous LEO Satellite Networks. IEEE Transactions on Parallel and Distributed Systems.
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Yuan, H., Chen, Z., Lin, Z., Peng, J., Fang, Z., Zhong, Y., Song, Z., & Gao, Y. (2025). SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing. IEEE Transactions on Cognitive Communications and Networking