Hao Zhang

Sep. 2019 – Sep. 2025
Ph.D. in Information and Communication Engineering, Institute of Information and Intelligence, Shanghai Jiao Tong University, Shanghai, China

Sep. 2015 – Jun. 2019
B.Eng. in Communication Engineering, School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China

Dec. 2025 – present

Zijiang Young Scholar, School of Communication and Electronic Engineering, East China Normal University, Shanghai, China

Hao Zhang is currently a Zijiang Young Scholar at the School of Communication and Electronic Engineering, East China Normal University. He received the B.Eng. degree from the University of Electronic Science and Technology of China in 2019 and the Doctor of Engineering degree in Electronic Engineering from Shanghai Jiao Tong University in 2025. His research interests include federated learning, distributed optimization, distributed post-training of large-scale models, information bottleneck methods, and Bayesian learning. His current work focuses on developing theoretical frameworks for convergence rates and generalization errors in distributed learning for edge–cloud collaborative training of large models, with an emphasis on elucidating how data heterogeneity and privacy constraints fundamentally affect the optimal error bounds.

Federated learning, Distributed optimization, Bayesian Learning, Distributed LLM fine-tuning

1. Hao Zhang, Chenglin Li, Wenrui Dai, Ziyang Zheng, Junni Zou, Hongkai Xiong, “Stabilizing and Accelerating Federated Learning on Heterogeneous Data with Partial Client Participation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 47, no. 1, pp. 67-83, Jan. 2025. (CCF-A)

2. Hao Zhang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, “Federated Learning Based on Model Discrepancy and Variance Reduction”, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 36, no. 6, pp. 10407-10421, June 2025.

3. Hao Zhang, Chenglin Li, Nuowen Kan, Ziyang Zheng, Wenrui Dai, Junni Zou, Hongkai Xiong, “Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach”, Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Dec. 2024. (CCF-A)

4. Hao Zhang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, “FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization”, International Conference on Machine Learning (ICML 2023), July 2023. (CCF-A) 

5. Li Ding*, Hao Zhang*, Wenrui Dai, Chenglin Li, Weijia Lu, Zhifei Yang, Xiaodong Zhang, Xiaofeng Ma, Junni Zou, Hongkai Xiong, “LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation”, International Conference on Machine Learning (ICML 2025), July 2025. (CCF-A) (co-author)

6. Xinyi Lu*, Hao Zhang*, Chenglin Li, Weijia Lu, Zhifei Yang, Wenrui Dai, Xiaodong Zhang, Xiaofeng Ma, Can Zhang, Junni Zou, Hongkai Xiong, “FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates”, International Conference on Machine Learning (ICML 2025), July 2025. (CCF-A) (co-author)