Zhaomin Wu

Dr. Zhaomin Wu (吴肇敏) is a Research Fellow at the Department of Computer Science, National University of Singapore (NUS). He completed his Ph.D. in Computer Science at the National University of Singapore (NUS) in 2024 advised by Prof. Bingsheng He. He received his Bachelor’s degree from Huazhong University of Science and Technology (HUST) in 2019.

Dr. Wu’s research focuses on trustworthy machine learning, with specific interests in trustworthy AI, federated learning, and machine unlearning. His work has been recognized with awards such as the SIGMOD Honorable Mention for Best Artifact (2023), the Dean’s Graduate Research Excellence Award (NUS, 2023), an Honorable Mention for Best Ph.D. Thesis Award (NUS, 2024), and the Best Research Staff Award (NUS, 2025). His publications appear in top-tier venues including NeurIPS, ICLR, SIGMOD, KDD, WWW, ACL, EMNLP, AAAI, MLSys, and TKDE.

News

Feb 08, 2026	Two papers accepted to ICLR 2026 for oral (1%) presentation. [Oral] Zhaomin Wu, Haodong Zhao, Ziyang Wang, Jizhou Guo, Qian Wang, Bingsheng He. LLM DNA: Tracing Model Evolution via Functional Representations [Oral] Zhaomin Wu, Mingzhe Du, Ng See-Kiong, Bingsheng He. Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
Jan 26, 2026	I was invited to give a keynote entitled “Bridging Data Silos with Practical Federated Learning” at the AAAI 2026 FLCA Workshop.
Jan 13, 2026	One paper accepted in WWW 2026. Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng. Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures.
Nov 24, 2025	One paper accepted in KDD 2026. Jizhou Guo, Zhaomin Wu, Hanchen Yang, Philip S. Yu. Mining Intrinsic Rewards from LLM Hidden States for Efficient Best-of-N Sampling
Aug 21, 2025	One paper accepted in EMNLP 2025. Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang. Model-based Large Language Model Customization as Service
May 29, 2025	I delivered an invited talk entitled “Towards Practical Vertical Federated Learning Systems” on behalf of Prof. Bingsheng He at the DASFAA 2025 Trust Day.
May 17, 2025	One paper accepted in ACL 2025. Zhen Qin, Zhaomin Wu, Bingsheng He, Shuiguang Deng. Federated Data-Efficient Instruction Tuning for Large Language Models.
Feb 25, 2025	I delivered an invited talk “Bridging Private Data Silo with Machine Learning” at the NUS Open House 2025.
Jan 12, 2025	I have received the Best Research Staff Award from Institute of Data Science, NUS.
Sep 28, 2024	One paper accepted in NeurIPS 2024. Zhaomin Wu, Junyi Hou, Yiqun Diao, and Bingsheng He. Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Jul 02, 2024	I have been awarded PhD Thesis Award Honorable Mention by the School of Computing (SoC), NUS.
May 30, 2024	Our paper “DeltaBoost: Gradient Boosted Trees with Efficient Machine Unlearning” (by Zhaomin Wu, Junhui Zhu, Qinbin Li, Bingsheng He) has been awarded the Honorable Mention for the Best Artifact in SIGMOD 2023.
May 03, 2024	I have passed my Ph.D. defense! 🎉
Jan 16, 2024	One paper accepted in ICLR 2024. Zhaomin Wu, Junyi Hou, Bingsheng He. VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Aug 10, 2023	I have received the Dean’s Graduate Research Excellence Award from School of Computing, NUS for academic year 2022/2023.

Selected Publications

ICLR 2026

LLM DNA: Tracing Model Evolution via Functional Representations

Zhaomin Wu, Haodong Zhao, Ziyang Wang, Jizhou Guo, Qian Wang, and Bingsheng He

In The Fourteenth International Conference on Learning Representations (ICLR), 2026

ICLR 2026 Oral (1%) Bib PDF

This paper was selected for oral presentation (1%) at ICLR 2026

@inproceedings{wu2026llmdna,
  title = {LLM DNA: Tracing Model Evolution via Functional Representations},
  author = {Wu, Zhaomin and Zhao, Haodong and Wang, Ziyang and Guo, Jizhou and Wang, Qian and He, Bingsheng},
  booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=UIxHaAqFqQ},
}

ICLR 2026

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

Zhaomin Wu, Mingzhe Du, Ng See-Kiong, and Bingsheng He

In The Fourteenth International Conference on Learning Representations (ICLR), 2026

ICLR 2026 Oral (1%) Bib PDF

This paper was selected for oral presentation (1%) at ICLR 2026

@inproceedings{wu2026deception,
  title = {Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts},
  author = {Wu, Zhaomin and Du, Mingzhe and See-Kiong, Ng and He, Bingsheng},
  booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=PDBBYwd1LY},
}

EMNLP 2025
Model-based Large Language Model Customization as Service

Zhaomin Wu^*, Jizhou Guo^*, Junyi Hou, Bingsheng He, Lixin Fan, and Qiang Yang

In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025

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Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially private (DP) data synthesis presents a potential alternative, its application commonly results in low effectiveness due to the introduction of excessive noise on data for DP. To overcome this, we introduce Llamdex, a novel framework that facilitates LLM customization as a service, where the client uploads pre-trained domain-specific models rather than data. This client-uploaded model, optionally protected by DP with much lower noise, is inserted into the base LLM via connection modules. Significantly, these connecting modules are trained without requiring sensitive domain data, enabling clients to customize LLM services while preserving data privacy. Experiments demonstrate that Llamdex improves domain-specific accuracy by up to 26% over state-of-the-art private data synthesis methods under identical privacy constraints and, by obviating the need for users to provide domain context within queries, maintains inference efficiency comparable to the original LLM service.
@inproceedings{wu2025llamdex, author = {Wu, Zhaomin and Guo, Jizhou and Hou, Junyi and He, Bingsheng and Fan, Lixin and Yang, Qiang}, title = {Model-based Large Language Model Customization as Service}, booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2025}, }
NeurIPS 2024
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data

Zhaomin Wu, Junyi Hou, Yiqun Diao, and Bingsheng He

In Advances in Neural Information Processing Systems, 2024

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Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Vertical Federated Learning (VFL), which involves multiple parties contributing distinct features of a shared instance group, is prevalent in real-world, cross-organizational collaborations. In such setups, parties are typically linked by fuzzy identifiers, a common scenario in practice termed as \textitmulti-party fuzzy VFL. Existing models generally address either multi-party VFL or fuzzy VFL between two parties. Extending these models to the practical multi-party fuzzy VFL typically results in significant performance degradation and increased costs for maintaining privacy. To overcome these limitations, we introduce the \textitFederated Transformer (FeT), a novel framework designed to support multi-party VFL with fuzzy identifiers. FeT encodes identifiers into data representations and conducts training using a transformer architecture distributed across different parties, incorporating three new techniques to enhance performance. Additionally, we have developed a scalable privacy framework that integrates differential privacy with secure multi-party computation, effectively protecting local representations at manageable costs. Experiments show that the FeT surpasses the performance of baseline models by up to 46 percentage points when scaled to 50 parties. Additionally, FeT outperforms cutting-edge models in two-party fuzzy VFL settings, while offering improved privacy.
@inproceedings{wu2024fet, author = {Wu, Zhaomin and Hou, Junyi and Diao, Yiqun and He, Bingsheng}, booktitle = {Advances in Neural Information Processing Systems}, publisher = {Curran Associates, Inc.}, title = {Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data}, volume = {36}, year = {2024}, }
ICLR 2024
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

Zhaomin Wu, Junyi Hou, and Bingsheng He

In The Twelfth International Conference on Learning Representations, 2024

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Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmarks often resort to synthetic datasets, derived from arbitrary feature splits from a global set, which only capture a subset of feature distributions, leading to inadequate algorithm performance assessment. This paper addresses these shortcomings by introducing two key factors affecting VFL performance - feature importance and feature correlation - and proposing associated evaluation metrics and dataset splitting methods. Additionally, we introduce a real VFL dataset to address the deficit in image-image VFL scenarios. Our comprehensive evaluation of cutting-edge VFL algorithms provides valuable insights for future research in the field.
@inproceedings{wu2024vertibench, title = {VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks}, author = {Wu, Zhaomin and Hou, Junyi and He, Bingsheng}, booktitle = {The Twelfth International Conference on Learning Representations}, year = {2024}, url = {https://openreview.net/forum?id=glwwbaeKm2}, }
SIGMOD 2023
DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning

Zhaomin Wu, Junhui Zhu, Qinbin Li, and Bingsheng He

Proc. ACM Manag. Data, 2023

Honorable Mention for Best Artifact Abs DOI Bib PDF Supp Code

DeltaBoost has won Honorable Mention for Best Artifact Award in SIGMOD 2023

As machine learning (ML) has been widely developed in real-world applications, the privacy of ML models draws an increasing concern. In this paper, we study how to forget specific data records from ML models to preserve the privacy of these data. Although some studies propose efficient unlearning algorithms on random forests and extremely randomized trees, Gradient Boosting Decision Trees (GBDT), which are widely used in practice, have not been explored. The efficient unlearning of GBDT faces two major challenges: 1) the training of each tree is deterministic and non-robust; 2) the training of a tree depends on all the previous trees. To solve the first challenge, we propose a robust GBDT-like ML model DeltaBoost that enables efficient and accurate deletion according to our theoretical analysis. For the second challenge, we design a training algorithm for DeltaBoost that minimizes the dependency among trees. Our experiments on five datasets demonstrate that DeltaBoost can remove data records from the trained model efficiently and effectively. Our unlearning approach achieves up to two orders of magnitude speedup compared to retraining GBDT. Besides, DeltaBoost produces competitive performance to existing decision-tree-based ML models.
@article{10.1145/3589313, author = {Wu, Zhaomin and Zhu, Junhui and Li, Qinbin and He, Bingsheng}, title = {DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning}, year = {2023}, issue_date = {June 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {2}, url = {https://doi.org/10.1145/3589313}, doi = {10.1145/3589313}, journal = {Proc. ACM Manag. Data}, articleno = {168}, numpages = {26}, keywords = {data deletion, gradient boosting decision trees, machine unlearning}, }
NeurIPS 2022
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning

Zhaomin Wu, Qinbin Li, and Bingsheng He

In Advances in Neural Information Processing Systems, 2022

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Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, most existing studies in VFL disregard the" record linkage”process. They design algorithms either assuming the data from different parties can be exactly linked or simply linking each record with its most similar neighboring record. These approaches may fail to capture the key features from other less similar records. Moreover, such improper linkage cannot be corrected by training since existing approaches provide no feedback on linkage during training. In this paper, we design a novel coupled training paradigm, FedSim, that integrates one-to-many linkage into the training process. Besides enabling VFL in many real-world applications with fuzzy identifiers, FedSim also achieves better performance in traditional VFL tasks. Moreover, we theoretically analyze the additional privacy risk incurred by sharing similarities. Our experiments on eight datasets with various similarity metrics show that FedSim outperforms other state-of-the-art baselines. The codes of FedSim are available at https://github. com/Xtra-Computing/FedSim.
@inproceedings{NEURIPS2022_84b74416, author = {Wu, Zhaomin and Li, Qinbin and He, Bingsheng}, booktitle = {Advances in Neural Information Processing Systems}, editor = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.}, pages = {21087--21100}, publisher = {Curran Associates, Inc.}, title = {A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/84b744165a0597360caad96b06e69313-Paper-Conference.pdf}, volume = {35}, year = {2022}, }