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 24 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Trading Off Privacy, Utility, and Efficiency in Federated Learning.
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

FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.
CoRR, 2023

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning.
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

FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training.
ACM Trans. Intell. Syst. Technol., 2022

Vertical Federated Learning.
CoRR, 2022

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning.
CoRR, 2022

A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning.
CoRR, 2022

Accelerating Vertical Federated Learning.
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
Defending Label Inference and Backdoor Attacks in Vertical Federated Learning.
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

Federated Deep Learning with Bayesian Privacy.
CoRR, 2021

2020
A Secure Federated Transfer Learning Framework.
IEEE Intell. Syst., 2020

FedML: A Research Library and Benchmark for Federated Machine Learning.
CoRR, 2020

2019
Federated Learning
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.
CoRR, 2019


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