An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning

23 February 2020

Papers citing "An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning"

7 / 7 papers shown

Title
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 32 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 47 19 0 27 Nov 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma Jianfeng Ma FedML 37 2 0 06 May 2023
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks Chung-ju Huang Leye Wang Xiao Han FedML 32 24 0 11 Feb 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei Chen Shuicheng Yan Min-Bin Lin FedML 36 10 0 28 Jan 2023
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations Jialing Liao Zheng Chen Erik G. Larsson 25 12 0 05 Oct 2022