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.CR2024

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning

Yixuan Liu, Li Xiong, Yuhan Liu +3

Differentially Private Stochastic Gradients Descent (DP-SGD) is a prominent paradigm for preserving privacy in deep learning. It ensures privacy by perturbing gradients with random…

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…