3 papers
cs.LG2024
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
Yan Kang, Jiahuan Luo, Yuanqin He +3
Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), ha…
cs.LG2024
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
Yuezhou Wu, Yan Kang, Jiahuan Luo +2
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstra…
cs.CR2024
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +5
Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The conce…