Yan Kang
Orcid: 0000-0002-2016-9503Affiliations:
- 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 24 papers
between 2019 and 2023.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2023
ACM Trans. Intell. Syst. Technol., December, 2023
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning.
CoRR, 2023
Grounding Foundation Models through Federated Transfer Learning: A General Framework.
CoRR, 2023
CoRR, 2023
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning.
CoRR, 2023
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning.
CoRR, 2023
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
2022
FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features.
IEEE Trans. Signal Process., 2022
ACM Trans. Intell. Syst. Technol., 2022
CoRR, 2022
CoRR, 2022
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
2021
CoRR, 2021
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability.
CoRR, 2021
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning.
CoRR, 2021
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning.
CoRR, 2021
2020
2019
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01585-4, 2019