Differentially Private Federated Learning: A Systematic Review

no code implementations • 14 May 2024 • Jie Fu, Yuan Hong, XinPeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen, Yang Cao

To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of differential privacy and federated scenarios.

Federated Learning Privacy Preserving

DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering

no code implementations • 6 Nov 2023 • Huifa Li, Jie Fu, Zhili Chen, Xiaomin Yang, Haitao Liu, XinPeng Ling

Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression.

Clustering

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations

no code implementations • 21 Aug 2023 • XinPeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, Zhili Chen

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data.

Federated Learning

SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing

no code implementations • 14 Nov 2022 • Jie Fu, Zhili Chen, XinPeng Ling

Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition.

Image Classification