358 citations · 493 across the 10 of their papers we have counts for
7 papers · 1 filter
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Yan Kang, Hanlin Gu, Xingxing Tang +7
Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneousl…
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…
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…
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning
Yang Liu, Tianyuan Zou, Yan Kang +4
In a vertical federated learning (VFL) scenario where features and model are split into different parties, communications of sample-specific updates are required for correct gradie…
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
Yan Kang, Yang Liu, Yuezhou Wu +2
We present a novel privacy-preserving federated adversarial domain adaptation approach () to address an under-studied but practical cross-silo federated domain adap…
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…