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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"

32 / 32 papers shown
Title
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
Kecen Li
Chen Gong
Xiaochen Li
Yuzhong Zhao
Xinwen Hou
Tianhao Wang
39
1
0
02 Apr 2025
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
52
0
0
20 Mar 2025
Masked Differential Privacy
Masked Differential Privacy
David Schneider
Sina Sajadmanesh
Vikash Sehwag
Saquib Sarfraz
Rainer Stiefelhagen
Lingjuan Lyu
Vivek Sharma
28
1
0
22 Oct 2024
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
39
0
0
03 Oct 2024
Privacy-Preserving Student Learning with Differentially Private
  Data-Free Distillation
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Bochao Liu
Jianghu Lu
Pengju Wang
Junjie Zhang
Dan Zeng
Zhenxing Qian
Shiming Ge
20
1
0
19 Sep 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
42
9
0
04 Sep 2024
Learning Differentially Private Diffusion Models via Stochastic
  Adversarial Distillation
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu
Pengju Wang
Shiming Ge
46
1
0
27 Aug 2024
Advances and Open Challenges in Federated Learning with Foundation
  Models
Advances and Open Challenges in Federated Learning with Foundation Models
Chao Ren
Han Yu
Hongyi Peng
Xiaoli Tang
Anran Li
...
A. Tan
Bo Zhao
Xiaoxiao Li
Zengxiang Li
Qiang Yang
FedML
AIFin
AI4CE
75
7
0
23 Apr 2024
Crafter: Facial Feature Crafting against Inversion-based Identity Theft
  on Deep Models
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
Shiming Wang
Zhe Ji
Liyao Xiang
Hao Zhang
Xinbing Wang
Cheng Zhou
Bo-wen Li
16
4
0
14 Jan 2024
A Unified View of Differentially Private Deep Generative Modeling
A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen
Raouf Kerkouche
Mario Fritz
SyDa
21
4
0
27 Sep 2023
PA-iMFL: Communication-Efficient Privacy Amplification Method against
  Data Reconstruction Attack in Improved Multi-Layer Federated Learning
PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning
Jianhua Wang
Xiaolin Chang
Jelena Mivsić
Vojislav B. Mivsić
Zhi Chen
Junchao Fan
39
2
0
25 Sep 2023
SoK: Privacy-Preserving Data Synthesis
SoK: Privacy-Preserving Data Synthesis
Yuzheng Hu
Fan Wu
Yue Liu
Yunhui Long
Gonzalo Munilla Garrido
Chang Ge
Bolin Ding
David A. Forsyth
Bo-wen Li
D. Song
54
25
0
05 Jul 2023
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized
  Language Model Finetuning Using Shared Randomness
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness
E. Zelikman
Qian Huang
Percy Liang
Nick Haber
Noah D. Goodman
62
14
0
16 Jun 2023
Model Conversion via Differentially Private Data-Free Distillation
Model Conversion via Differentially Private Data-Free Distillation
Bochao Liu
Pengju Wang
Shikun Li
Dan Zeng
Shiming Ge
FedML
15
3
0
25 Apr 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward
  Phase
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Chih-Hsun Lin
Chia-Yi Hsu
Chia-Mu Yu
Yang Cao
Chun-ying Huang
24
1
0
20 Apr 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images
Differentially Private Diffusion Models Generate Useful Synthetic Images
Sahra Ghalebikesabi
Leonard Berrada
Sven Gowal
Ira Ktena
Robert Stanforth
Jamie Hayes
Soham De
Samuel L. Smith
Olivia Wiles
Borja Balle
DiffM
31
69
0
27 Feb 2023
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
33
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
33
7
0
22 Feb 2023
Private GANs, Revisited
Private GANs, Revisited
Alex Bie
Gautam Kamath
Guojun Zhang
19
14
0
06 Feb 2023
Differentially Private Kernel Inducing Points using features from
  ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Margarita Vinaroz
M. Park
DD
22
0
0
31 Jan 2023
Private Set Generation with Discriminative Information
Private Set Generation with Discriminative Information
Dingfan Chen
Raouf Kerkouche
Mario Fritz
DD
27
34
0
07 Nov 2022
Differentially Private Diffusion Models
Differentially Private Diffusion Models
Tim Dockhorn
Tianshi Cao
Arash Vahdat
Karsten Kreis
DiffM
32
91
0
18 Oct 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
30
3
0
22 Sep 2022
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future
  Directions
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Chulin Xie
Zhong Cao
Yunhui Long
Diange Yang
Ding Zhao
Bo-wen Li
16
4
0
08 Sep 2022
DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
DP2^22-VAE: Differentially Private Pre-trained Variational Autoencoders
Dihong Jiang
Guojun Zhang
Mahdi Karami
Xi Chen
Yunfeng Shao
Yaoliang Yu
41
15
0
05 Aug 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
30
16
0
20 Jul 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
19
62
0
15 Feb 2022
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data
  Release
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
Donghao Li
Yang Cao
Yuan Yao
35
2
0
14 Feb 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
29
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
26
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
18
0
21 Apr 2021
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