GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning

2 May 2021

Papers citing "GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning"

9 / 9 papers shown

Title
Ten Challenging Problems in Federated Foundation Models Tao Fan Hanlin Gu Xuemei Cao Chee Seng Chan Qian Chen ... Y. Zhang Xiaojin Zhang Zhenzhe Zheng Lixin Fan Qiang Yang FedML 83 4 0 14 Feb 2025
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 30 0 0 11 Oct 2024
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng FedML AAML 40 19 0 27 Nov 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma J. Ma FedML 24 2 0 06 May 2023
Digital Privacy Under Attack: Challenges and Enablers Baobao Song Mengyue Deng Shiva Raj Pokhrel Qiujun Lan R. Doss Gang Li AAML 36 3 0 18 Feb 2023
Federated Learning Attacks and Defenses: A Survey Yao Chen Yijie Gui Hong Lin Wensheng Gan Yongdong Wu FedML 38 29 0 27 Nov 2022
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks Xingjian Cao Gang Sun Hongfang Yu Mohsen Guizani FedML 18 56 0 18 Feb 2022
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning Yuezhou Wu Yan Kang Jiahuan Luo Yuanqin He Qiang Yang FedML AAML 19 68 0 16 Nov 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021