358 citations · 1.1k across the 26 of their papers we have counts for
6 papers · 1 filter
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou +6
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models w…
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…
Trading Off Privacy, Utility and Efficiency in Federated Learning
Xiaojin Zhang, Yan Kang, Kai Chen +2
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
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…
Accelerating Vertical Federated Learning
Dongqi Cai, Tao Fan, Yan Kang +4
Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Fe…
Amalur: Data Integration Meets Machine Learning
Rihan Hai, Christos Koutras, Andra Ionescu +5
The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a majo…