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Haoran Zhang Hi! I'm currently a second-year MS student in Electrical and Computer Engineering Department at Carnegie Mellon University (CMU). Now I am working with Prof. Carlee Joe-Wong on federated learning. I am also working with Dr. Marie Siew and Prof. Rachid El-Azouzi in the current research project. Previously, I obtained my bachelor's degree in Automation from Huazhong University of Science and Technology. During the undergrad study, I worked on AI for healthcare with Prof. Hao Chen at HKUST, and Dr. Zhongliang Jiang at TUM. I will graduate from CMU in May 2025, and I am looking for a Ph.D. position starting from Fall 2025. |
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ResearchMy current project is about multi-model federated learning (MMFL): local clients can handle multiple unrelated FL models concurrently. For example, a smartphone user can generate data for both keyboard prediction and speech recognition models. Instead of building two single-model FL systems, we build one MMFL system and solve system-level optimization problems to make it more efficient. |
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My research at CMU: Federated Learning and Optimization |
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[On-going project] Client sampling in a more heterogeneous MMFL scenario. How to optimize the sampling distribution? |
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Group-based Client Sampling in Multi-Model Federated Learning
Zejun Gong*, Haoran Zhang*, Marie Siew, Carlee Joe-Wong, Rachid El-Azouzi Under Review (ICASSP), 2024. *Equal contribution
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Poster: Optimal Variance-Reduced Client Sampling for Multiple Models Federated Learning
Haoran Zhang, Zekai Li, Zejun Gong, Marie Siew, Carlee Joe-Wong, Rachid El-Azouzi ICDCS, 2024 [Best Poster Award] [Poster] Supplementary material This paper explores an MMFL scenario where each client is restricted to training only one model per global round due to limited computational resources. We propose a client sampling and task assignment method that theoretically minimizes the variance of global updates. The method achieves a 30% higher accuracy than the baseline. |
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Fair Concurrent Training of Multiple Models in Federated Learning
Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu, Yichen Ruan, Lili Su, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong arXiv, 2024 Supplementary material In the real-world scenario, clients could contribute to multiple federated learning models' training at the same time. For example, a company may update their keyboard prediction model, speech recognition model, and other more federated learning models on your phone. In this paper, we propose a client-task assignment strategy to preserve the fairness across multiple training tasks. |
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My research at HKUST: Medical AI |
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Efficient 3D Transformer with cluster-based Domain-Adversarial Learning for 3D Medical Image Segmentation
Haoran Zhang, Hao Chen ISBI (one of the best conferences in medical image processing), 2023 We proposed a pyramidally downsampled 3D Transformer to boost efficiency and maintain high segmentation accuracy. This paper also discussed the domain generalization problem, and proposed a cluster-based domain-adversarial learning method to exploit available domains at a fine-grained level. |
Some Presentations |
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Topics on Multi-Model Federated Learning (about our ICDCS poster)
CMU LIONS group seminar, May 17, 2024 |
Education
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About MeI was born in Xinxiang, China. My family name is 张 (Zhang), which is one of the most common family names in China. And my given name is 皓然 (Haoran), 皓(Hao) means "the color of the moon", 然(Ran) doesn't have a specific meaning, maybe it means "yeah, uh-huh". As the first in my family to attend college, I am deeply grateful for the support from my parents, mentors, and friends. |
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Template stolen from Jon Barron.
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