Yan Kang

Orcid: 0000-0002-2016-9503

Affiliations:

WeBank, Department of Artificial Intelligence, Shenzhen, China
Hong Kong University of Science and Technology, Hong Kong
University of Maryland Baltimore County, MD, USA (PhD)

According to our database¹, Yan Kang authored at least 43 papers between 2019 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2025

FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models.

[BibT_eX]

[DOI]

Proceedings of the 31st International Conference on Computational Linguistics, 2025

2024

Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework.

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., December, 2024

Defending Batch-Level Label Inference and Replacement Attacks in Vertical Federated Learning.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, December, 2024

Privacy-Preserving Federated Adversarial Domain Adaptation Over Feature Groups for Interpretability.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, December, 2024

Accelerating Vertical Federated Learning.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, December, 2024

Vertical Federated Learning: Concepts, Advances, and Challenges.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., July, 2024

A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning.

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., June, 2024

FedCoLLM: A Parameter-Efficient Federated Co-tuning Framework for Large and Small Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation.

[BibT_eX]

[DOI]

CoRR, 2024

No Free Lunch Theorem for Privacy-Preserving LLM Inference.

[BibT_eX]

[DOI]

CoRR, 2024

FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom.

[BibT_eX]

[DOI]

CoRR, 2024

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2024

SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning.

[BibT_eX]

[DOI]

Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

Personalized Federated Continual Learning via Multi-Granularity Prompt.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Model Trip: Enhancing Privacy and Fairness in Model Fusion Across Multi-Federations for Trustworthy Global Healthcare.

[BibT_eX]

[DOI]

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Trading Off Privacy, Utility, and Efficiency in Federated Learning.

[BibT_eX]

[DOI]

ACM Trans. Intell. Syst. Technol., December, 2023

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Grounding Foundation Models through Federated Transfer Learning: A General Framework.

[BibT_eX]

[DOI]

CoRR, 2023

Privacy in Large Language Models: Attacks, Defenses and Future Directions.

[BibT_eX]

[DOI]

CoRR, 2023

FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2022

FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training.

[BibT_eX]

[DOI]

Yan Kang

Yang Liu

Xinle Liang

ACM Trans. Intell. Syst. Technol., 2022

Vertical Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

Defending Label Inference and Backdoor Attacks in Vertical Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability.

[BibT_eX]

[DOI]

CoRR, 2021

FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2021

SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2021

Federated Deep Learning with Bayesian Privacy.

[BibT_eX]

[DOI]

CoRR, 2021

2020

A Secure Federated Transfer Learning Framework.

[BibT_eX]

[DOI]

IEEE Intell. Syst., 2020

FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training.

[BibT_eX]

[DOI]

Yan Kang

Yang Liu

Tianjian Chen

CoRR, 2020

FedML: A Research Library and Benchmark for Federated Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Federated Learning

[BibT_eX]

[DOI]

Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01585-4, 2019

A Communication Efficient Vertical Federated Learning Framework.

[BibT_eX]

[DOI]

CoRR, 2019

Secure and Efficient Federated Transfer Learning.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Yan Kang

Timeline

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