74 citations · 169 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning
Yuanqin He, Yan Kang, Xinyuan Zhao +4
Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valu…
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…
Self-supervised Cross-silo Federated Neural Architecture Search
Xinle Liang, Yang Liu, Jiahuan Luo +3
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties. In the traini…