6 citations · 13 across the 4 of their papers we have counts for
4 papers
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature
Wenyuan Yang, Gongxi Zhu, Yuguo Yin +4
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data. The high cost of training and the significant value of the glob…
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
Wenyuan Yang, Yuguo Yin, Gongxi Zhu +4
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models…
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…
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +2
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about t…