Department of Electronic Engineering, the Chinese University of Hong Kong.
Ho Sin Hang Engineering Building
Shatin, N.T., Hong Kong
I’m working closely with Prof. Bo Han and Dr.Yonggang Zhang at the TMLR group, HKBU . Prior to this, I earned my M.S. degree from Beihang University (BUAA), advised by Prof. Prof. Hao Peng. I also completed my B.S. degree at Nanjing University of Science and Technology (NJUST), where I spent four enriching years. My research focuses on Federated Learning and its applications, particularly in privacy-preserving healthcare solutions.
Beyond academia, I am an avid basketball enthusiast and a devoted fan of Kobe Bryant. I enjoy immersing myself in live music, especially hip-hop and R&B, with Nous Underground from XAC being a favorite. Additionally, I have a deep interest in Chinese history, particularly the Ming Dynasty.
If you’re interested in discussing potential collaborations or shared passions, please feel free to reach out! I welcome diverse perspectives and ideas to broaden me.
Please feel free to contact me with any form of communication and collaborations!!!
Two papers about Federated Learning on Heterogeneity and Benchmarking VLM on Spatial Understanding have been accepted by NeurIPS’25. Welcome to check our paper: FedGPS, IR3D. See you in San Diego
Jun 18, 2025
We release LearnAlign to select high-quality data for LLM post-training. Welcome to check our paper
Jul 01, 2024
I graduate from Beihang University with Outstanding Master Thesis Award
@article{yang2024fedfed,title={FedFed: Feature distillation against data heterogeneity in federated learning},author={Yang, Zhiqin and Zhang, Yonggang and Zheng, Yu and Tian, Xinmei and Peng, Hao and Liu, Tongliang and Han, Bo},journal={Advances in Neural Information Processing Systems},volume={36},year={2023},github_star={https://img.shields.io/github/stars/visitworld123/FedFed.svg}}
NeurIPS 2025
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Chenxin Li, Yiu-ming Cheung, Bo Han, and 1 more author
Advances in Neural Information Processing Systems, 2025
@article{yang2025fedgps,title={FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning},author={Yang, Zhiqin and Zhang, Yonggang and Li, Chenxin and Cheung, Yiu-ming and Han, Bo and Yuan, Yixuan},journal={Advances in Neural Information Processing Systems},year={2025},}
ICLR 2024
Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang*, Zhiqin Yang*, Xinmei Tian, Nannan Wang, Tongliang Liu, and 1 more author
In The Twelfth International Conference on Learning Representations, 2024
@inproceedings{zhangrobust,title={Robust Training of Federated Models with Extremely Label Deficiency},author={Zhang, Yonggang and Yang, Zhiqin and Tian, Xinmei and Wang, Nannan and Liu, Tongliang and Han, Bo},booktitle={The Twelfth International Conference on Learning Representations},year={2024},github_star={https://img.shields.io/github/stars/visitworld123/Twin-sight.svg}}
arXiv 2506.11480
LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment
Shikun Li*, Zhiqin Yang*, Shipeng Li*, Xinghua Zhang, Gaode Chen, and 3 more authors
@article{li2025learnalign,title={LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment},author={Li, Shikun and Yang, Zhiqin and Li, Shipeng and Zhang, Xinghua and Chen, Gaode and Xia, Xiaobo and Liu, Hengyu and Peng, Zhe},journal={arXiv preprint arXiv:2506.11480},year={2025},}
