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

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

collaborators

2 papers

cs.CR2023

PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance

Haichao Sha, Ruixuan Liu, Yixuan Liu +1

The paradigm of Differentially Private SGD~(DP-SGD) can provide a theoretical guarantee for training data in both centralized and federated settings. However, the utility degradati…

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…