FLAME: Differentially Private Federated Learning in the Shuffle Model

FLAME: Differentially Private Federated Learning in the Shuffle Model

17 September 2020

Ruixuan Liu

Yang Cao

Masatoshi Yoshikawa

Papers citing "FLAME: Differentially Private Federated Learning in the Shuffle Model"

17 / 17 papers shown

Title
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi Yaoliang Yu Yang Cao FedML 131 6 0 17 Feb 2025
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection Ruixuan Liu Yang Cao Masatoshi Yoshikawa Hong Chen FedML 41 108 0 24 Mar 2020
Pure Differentially Private Summation from Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Rasmus Pagh A. Velingker FedML 82 46 0 05 Feb 2020
Separating Local & Shuffled Differential Privacy via Histograms Victor Balcer Albert Cheu FedML 64 67 0 15 Nov 2019
Collecting and Analyzing Multidimensional Data with Local Differential Privacy Ning Wang Xiaokui Xiao Yifan Yang Jun Zhao S. Hui Hyejin Shin Junbum Shin Ge Yu 40 320 0 28 Jun 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 90 2,199 0 21 Jun 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 52 47 0 20 Jun 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 61 237 0 07 Mar 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 70 2,306 0 13 Feb 2019
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning Milad Nasr Reza Shokri Amir Houmansadr FedML MIACV AAML 48 250 0 03 Dec 2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 172 426 0 29 Nov 2018
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 83 352 0 04 Aug 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 77 385 0 04 Jul 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 112 1,293 0 20 Dec 2017
Sparse Communication for Distributed Gradient Descent Alham Fikri Aji Kenneth Heafield 63 740 0 17 Apr 2017
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Briland Hitaj G. Ateniese Fernando Perez-Cruz FedML 111 1,399 0 24 Feb 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 189 6,101 0 01 Jul 2016