Tao Fan
Orcid: 0009-0003-3040-6140Affiliations:
- WeBank, WeBank AI Group, Department of Artificial Intelligence, FATE, Shenzhen, China
- Hong Kong University of Science and Technology, Hong Kong, SAR, China
According to our database1,
Tao Fan authored at least 26 papers
between 2019 and 2026.
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Bibliography
2026
InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
2025
Grounding Foundation Models through Federated Transfer Learning: A General Framework.
ACM Trans. Intell. Syst. Technol., August, 2025
IEEE Trans. Knowl. Data Eng., July, 2025
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling.
CoRR, May, 2025
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study.
CoRR, May, 2025
Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions.
CoRR, April, 2025
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation.
CoRR, February, 2025
Federated Financial Reasoning Distillation: Training A Small Financial Expert by Learning From Multiple Teachers.
Proceedings of the 6th ACM International Conference on AI in Finance, 2025
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025
Proceedings of the 31st International Conference on Computational Linguistics, 2025
2024
Privacy-Preserving Federated Adversarial Domain Adaptation Over Feature Groups for Interpretability.
IEEE Trans. Big Data, December, 2024
FedCoLLM: A Parameter-Efficient Federated Co-tuning Framework for Large and Small Language Models.
CoRR, 2024
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models.
CoRR, 2024
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack.
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024
2023
Grounding Foundation Models through Federated Transfer Learning: A General Framework.
CoRR, 2023
CoRR, 2023
2021
J. Mach. Learn. Res., 2021
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning.
CoRR, 2021
2019
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression.
CoRR, 2019