Jingyang (William) Zhu 朱敬阳

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

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 6G, IoT and AI [Google Scholar].

Email: zhujy2@shanghaitech.edu.cn

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

News

Research

My research interests include

Distributed Learning and Optimization.
5G/6G, Wireless Communication, Signal Processing.
Satellite Networks.

Preprints

Y. Shi, L. Zeng, J. Zhu, Y. Zhou, C. Jiang, and K. B. Letaief, "Satellite Federated Edge Learning: Architecture Design and Convergence Analysis ", submitted to IEEE Transactions on Wireless Communications for possible publication.
H. Yang, S. Xia, J. Zhu, Y. Wu, Y. Zhou, and Y. Shi, "Provable Guarantees for Over-the-Air Federated Learning with Polynomial Neural Networks ", submitted to IEEE Transactions on Wireless Communications for possible publication.

Publications

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., early access, Jan. 2024. [pdf]
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]
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]
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 WCNC, March 2023, Glasgow, Scotland. [pdf]
S. Xia, J. Zhu, Y. Yang, Y. Zhou, Y. Shi and W. Chen, "Fast Convergence Algorithm for Analog Federated Learning," in Proc. IEEE ICC, 2021. [pdf]