iDLG: Improved Deep Leakage from Gradients

iDLG: Improved Deep Leakage from Gradients

8 January 2020

Konda Reddy Mopuri

Papers citing "iDLG: Improved Deep Leakage from Gradients"

11 / 111 papers shown

Title
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective Jingwei Sun Ang Li Binghui Wang Huanrui Yang Hai Li Yiran Chen FedML 27 163 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 357 0 07 Dec 2020
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 14 132 0 15 Oct 2020
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Enmao Diao Jie Ding Vahid Tarokh FedML 46 546 0 03 Oct 2020
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets Ang Li Jingwei Sun Binghui Wang Lin Duan Sicheng Li Yiran Chen H. Li FedML 28 125 0 07 Aug 2020
Dataset Condensation with Gradient Matching Bo Zhao Konda Reddy Mopuri Hakan Bilen DD 36 477 0 10 Jun 2020
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data Xinwei Zhang Mingyi Hong S. Dhople W. Yin Yang Liu FedML 23 228 0 22 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 75 25 0 27 Apr 2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning Wenqi Wei Ling Liu Margaret Loper Ka-Ho Chow Mehmet Emre Gursoy Stacey Truex Yanzhao Wu FedML 26 146 0 22 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 60 50 0 01 Apr 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 204 436 0 04 Mar 2020