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Jingyang (William) Zhu | 朱敬阳

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Hello everyone! I am a forth-year phd student advised by Prof. Yuanming Shi at School of Information Science and Technology (SIST), ShanghaiTech University, majoring in Computer Science (CS).

I was also in internship with the Nokia Bell Labs, Shanghai, China, May to September, 2025.

Earlier, I received my bachelor's degree in Electronic and Information Engineering from School of Electronic Engineering (SEE), Xidian University in 2021.

My research interest lies in the intersection of machine learning and communication networks, in particular federated learning, mathematical optimization as well as their applications to satellite edge AI and space computing power networks [Google Scholar].

Email: zhujy2@shanghaitech.edu.cn

Address: Rm. 2-309, SIST Building 2, 393 Middle Huaxia Road, Pudong New District, Shanghai 201210, China.

Research

My research interests include

  • Distributed Learning and Optimization.

  • Space Computing Power Networks.

Services

  • Reviewer of Journals

  1. IEEE Transactions on Mobile Computing

  2. IEEE Internet of Things Journal

  3. IEEE Transactions on Communications

  4. IEEE Transactions on Wireless Communications

  5. IEEE Open Journal of the Communications Society

  6. IEEE Wireless Communications Letters

Preprints

Publications (by year)

[1] Y. Zhu, J. Zhu, T. Wang, Y. Shi, C. Jiang, and K. B. Letaief, "Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks," IEEE Trans. Wireless Commun., early access, Nov. 2025. [pdf]

[2] Z. Wang, Y. Shi, Y. Zhou, J. Zhu, and K. B. Letaief, "Edge Large AI Models: Revolutionizing 6G Networks," IEEE Commun. Mag., vol. 63, no. 10, pp. 36 - 42, Sept. 2025. [pdf]

[3] Y. Shi, J. Zhu*, C. Jiang, L. Kuang, and K. B. Letaief, "Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies," Sci. China Inf. Sci., vol. 68, no. 7, pp. 1 - 15, Jul. 2025. [pdf]

[4] J. Zhu, Y. Shi, Y. Zhou, C. Jiang, and L. Kuang, "Hierarchical Learning and Computing over Space-Ground Integrated Networks," IEEE Trans. Mobile Comput., vol. 24, no. 10, pp. 10423 - 10440, Oct. 2025. [pdf] [code]

[5] Z. Yang, P. Zhang, J. Zhu, D. Wen, Y. Shi, and W. Chen, "Hierarchical Federated Learning with Integrated Sensing-Communication-Computation over Space-Air-Ground Integrated Networks", in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2025, Montreal, Canada. [pdf]

[6] R. Li, J. Zhu, Y. Mao, Y. Shi, T. Wang, C. Jiang, "Topology-Aware Routing for Federated Learning Over Multi-Layer Satellite Networks," in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2025, Milan, Italy. [pdf]

[7] Y. Zhu, P. Yang, J. Zhu, D. Wen, T. Wang, Y. Zhou, and C. Jiang, "Satellite Federated Fine-tuning for Foundation Models: Architecture Design and System Optimization", in Proc. IEEE Global Conf. Commun. (GLOBECOM), Dec.2024, Cape Town, South Africa. [pdf]

[8] Y. Shi, L. Zeng, J. Zhu, Y. Zhou, C. Jiang, and K. B. Letaief, "Satellite Federated Edge Learning: Architecture Design and Convergence Analysis," IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 15212 - 15229, Oct. 2024. [pdf]

[9] J. Zhu, Y. Shi, Y. Zhou, C. Jiang, W. Chen, and K. B. Letaief, "Over-the-Air Federated Learning and Optimization," IEEE Internet Things J., vol. 10, no. 11, pp. 16996 - 17020, May 2024. [pdf] [code]

[10] J. Zhu, Y. Shi, M. Fu, Y. Zhou, Y. Wu, and L. Fu, "Latency Minimization for Wireless Federated Learning with Heterogeneous Local Model Updates," IEEE Internet Things J., vol. 11, no. 1, pp. 444-461, Jan. 2024. [pdf]

[11] Y. Wang, J. Zhu, Y. Mao, D. Wen, X. Tian, and Y. Shi, "Hierarchical Federated Edge Learning over Space-Air-Ground Integrated Networks," in Proc. IEEE GLOBECOM WORKSHOPS (GC WKSHPS), Dec. 2023, Kuala Lumpur, Malaysia. [pdf]

[12] J. Zhu, Y. Shi, M. Fu, Y. Zhou, Y. Wu, and L. Fu, "Latency Minimization for Wireless Federated Learning with Heterogeneous Local Updates," in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2023, Glasgow, Scotland. [pdf]

[13] S. Xia, J. Zhu, Y. Yang, Y. Zhou, Y. Shi, and W. Chen, "Fast Convergence Algorithm for Analog Federated Learning," in Proc. IEEE Int. Conf. Commun. (ICC), Online, 2021. [pdf]