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DataLens: Scalable Privacy Preserving Training via Gradient Compression
  and Aggregation

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

20 March 2021
Boxin Wang
Fan Wu
Yunhui Long
Luka Rimanic
Ce Zhang
Bo-wen Li
    FedML
ArXivPDFHTML

Papers citing "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation"

13 / 13 papers shown
Title
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
Chia-Yi Hsu
Jia-You Chen
Yu-Lin Tsai
Chih-Hsun Lin
Pin-Yu Chen
Chia-Mu Yu
Chun-ying Huang
50
0
0
20 Mar 2025
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
PFGuard: A Generative Framework with Privacy and Fairness Safeguards
Soyeon Kim
Yuji Roh
Geon Heo
Steven Euijong Whang
31
0
0
03 Oct 2024
Learning Privacy-Preserving Student Networks via
  Discriminative-Generative Distillation
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
Shiming Ge
Bochao Liu
Pengju Wang
Yong Li
Dan Zeng
FedML
37
9
0
04 Sep 2024
Membership Inference Attacks against Synthetic Data through Overfitting
  Detection
Membership Inference Attacks against Synthetic Data through Overfitting Detection
B. V. Breugel
Hao Sun
Zhaozhi Qian
M. Schaar
19
45
0
24 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated
  Learning
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning
Van Tuan Tran
Huy Hieu Pham
Kok-Seng Wong
FedML
25
7
0
22 Feb 2023
Private GANs, Revisited
Private GANs, Revisited
Alex Bie
Gautam Kamath
Guojun Zhang
8
14
0
06 Feb 2023
Private Set Generation with Discriminative Information
Private Set Generation with Discriminative Information
Dingfan Chen
Raouf Kerkouche
Mario Fritz
DD
22
34
0
07 Nov 2022
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble
  Private Learning
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning
Jiaqi Wang
R. Schuster
Ilia Shumailov
David Lie
Nicolas Papernot
FedML
17
3
0
22 Sep 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient
  Communication with ADMM
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Chulin Xie
Pin-Yu Chen
Qinbin Li
Arash Nourian
Ce Zhang
Bo Li
FedML
25
16
0
20 Jul 2022
Don't Generate Me: Training Differentially Private Generative Models
  with Sinkhorn Divergence
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao
Alex Bie
Arash Vahdat
Sanja Fidler
Karsten Kreis
SyDa
DiffM
11
71
0
01 Nov 2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in
  Machine Learning
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Yansong Gao
Qun Li
Yifeng Zheng
Guohong Wang
Jiannan Wei
Mang Su
6
3
0
26 Oct 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Runhua Xu
Nathalie Baracaldo
J. Joshi
24
100
0
10 Aug 2021
ScaleCom: Scalable Sparsified Gradient Compression for
  Communication-Efficient Distributed Training
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen
Jiamin Ni
Songtao Lu
Xiaodong Cui
Pin-Yu Chen
...
Naigang Wang
Swagath Venkataramani
Vijayalakshmi Srinivasan
Wei Zhang
K. Gopalakrishnan
27
66
0
21 Apr 2021
1