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FedSel: Federated SGD under Local Differential Privacy with Top-k
  Dimension Selection

FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection

24 March 2020
Ruixuan Liu
Yang Cao
Masatoshi Yoshikawa
Hong Chen
    FedML
ArXiv (abs)PDFHTML

Papers citing "FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection"

21 / 21 papers shown
Title
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
Shaowei Wang
Changyu Dong
Xiangfu Song
Jin Li
Zhili Zhou
Di Wang
Han Wu
114
0
0
26 Jun 2024
Training Machine Learning models at the Edge: A Survey
Training Machine Learning models at the Edge: A Survey
Aymen Rayane Khouas
Mohamed Reda Bouadjenek
Hakim Hacid
Sunil Aryal
111
12
0
05 Mar 2024
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Natalie Lang
Nir Shlezinger
Rafael G. L. DÓliveira
S. E. Rouayheb
FedML
156
4
0
01 Aug 2023
A Survey of Trustworthy Federated Learning with Perspectives on
  Security, Robustness, and Privacy
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang
Dun Zeng
Jinglong Luo
Zenglin Xu
Irwin King
FedML
156
49
0
21 Feb 2023
DPD-fVAE: Synthetic Data Generation Using Federated Variational
  Autoencoders With Differentially-Private Decoder
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder
Bjarne Pfitzner
B. Arnrich
FedML
84
18
0
21 Nov 2022
Dordis: Efficient Federated Learning with Dropout-Resilient Differential
  Privacy
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy
Zhifeng Jiang
Wei Wang
Ruichuan Chen
76
7
0
26 Sep 2022
FedPerm: Private and Robust Federated Learning by Parameter Permutation
FedPerm: Private and Robust Federated Learning by Parameter Permutation
Hamid Mozaffari
Virendra J. Marathe
D. Dice
FedML
80
4
0
16 Aug 2022
Measuring Lower Bounds of Local Differential Privacy via Adversary
  Instantiations in Federated Learning
Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning
Marin Matsumoto
Tsubasa Takahashi
Seng Pei Liew
M. Oguchi
FedMLBDL
50
0
0
18 Jun 2022
Federated Learning with Sparsified Model Perturbation: Improving
  Accuracy under Client-Level Differential Privacy
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy
Rui Hu
Yanmin Gong
Yuanxiong Guo
FedML
87
72
0
15 Feb 2022
Improving Differentially Private SGD via Randomly Sparsified Gradients
Improving Differentially Private SGD via Randomly Sparsified Gradients
Junyi Zhu
Matthew B. Blaschko
63
5
0
01 Dec 2021
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based
  Vertical Federated Learning
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
Jinyin Chen
Guohan Huang
Haibin Zheng
Shanqing Yu
Wenrong Jiang
Chen Cui
AAMLFedML
130
32
0
13 Oct 2021
Lightweight Transformer in Federated Setting for Human Activity
  Recognition
Lightweight Transformer in Federated Setting for Human Activity Recognition
Ali Raza
K. Tran
L. Koehl
Shujun Li
Xianyi Zeng
Khaled Benzaidi
MedIm
72
10
0
01 Oct 2021
Optimizing the Numbers of Queries and Replies in Federated Learning with
  Differential Privacy
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
Yipeng Zhou
Xuezheng Liu
Yao Fu
Di Wu
Chao Li
Shui Yu
FedML
66
2
0
05 Jul 2021
Stronger Privacy for Federated Collaborative Filtering with Implicit
  Feedback
Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Lorenzo Minto
Moritz Haller
Hamed Haddadi
B. Livshits
FedML
67
74
0
09 May 2021
From Distributed Machine Learning to Federated Learning: A Survey
From Distributed Machine Learning to Federated Learning: A Survey
Ji Liu
Jizhou Huang
Yang Zhou
Xuhong Li
Shilei Ji
Haoyi Xiong
Dejing Dou
FedMLOOD
140
259
0
29 Apr 2021
Federated Learning with Local Differential Privacy: Trade-offs between
  Privacy, Utility, and Communication
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication
Muah Kim
Onur Gunlu
Rafael F. Schaefer
FedML
168
121
0
09 Feb 2021
Transparent Contribution Evaluation for Secure Federated Learning on
  Blockchain
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
Shuaicheng Ma
Yang Cao
L. Xiong
FedML
67
37
0
26 Jan 2021
On the Practicality of Differential Privacy in Federated Learning by
  Tuning Iteration Times
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
Yao Fu
Yipeng Zhou
Di Wu
Shui Yu
Yonggang Wen
Chao Li
FedML
51
10
0
11 Jan 2021
A Comprehensive Survey on Local Differential Privacy Toward Data
  Statistics and Analysis
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
Teng Wang
Xuefeng Zhang
Xuefeng Zhang
Xinyu Yang
96
88
0
11 Oct 2020
FLAME: Differentially Private Federated Learning in the Shuffle Model
FLAME: Differentially Private Federated Learning in the Shuffle Model
Ruixuan Liu
Yang Cao
Hong Chen
Ruoyang Guo
Masatoshi Yoshikawa
FedML
90
96
0
17 Sep 2020
Privacy-Preserving Classification with Secret Vector Machines
Privacy-Preserving Classification with Secret Vector Machines
Valentin Hartmann
Konark Modi
J. M. Pujol
Robert West
74
14
0
08 Jul 2019
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