most citedEcho of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

2 citations · 4 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CR2023★ 2 cited

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

Yixuan Liu, Suyun Zhao, Li Xiong +2

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfie…

cs.IR2022★ 2 cited

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

Ruixuan Liu, Yanlin Wang, Yang Cao +4

Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…