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1905.03871
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Differentially Private Learning with Adaptive Clipping
Neural Information Processing Systems (NeurIPS), 2019
9 May 2019
Galen Andrew
Om Thakkar
H. B. McMahan
Swaroop Ramaswamy
FedML
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Papers citing
"Differentially Private Learning with Adaptive Clipping"
50 / 201 papers shown
Gradient Leakage Attack Resilient Deep Learning
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2021
Wenqi Wei
Ling Liu
SILM
PILM
AAML
182
62
0
25 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients
Junyi Zhu
Matthew B. Blaschko
400
7
0
01 Dec 2021
Differentially private stochastic expectation propagation (DP-SEP)
Margarita Vinaroz
Mijung Park
309
1
0
25 Nov 2021
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble
A. Bellet
Hadrien Hendrikx
FedML
287
143
0
17 Nov 2021
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting
Joaquín Delgado Fernández
Sergio Potenciano Menci
Chul Min Lee
Gilbert Fridgen
279
70
0
17 Nov 2021
DP-REC: Private & Communication-Efficient Federated Learning
Aleksei Triastcyn
M. Reisser
Christos Louizos
FedML
160
18
0
09 Nov 2021
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Shubhankar Mohapatra
Sajin Sasy
Xi He
Gautam Kamath
Om Thakkar
287
35
0
09 Nov 2021
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups
Italian National Conference on Sensors (INS), 2021
A. Wainakh
Ephraim Zimmer
Sandeep Subedi
Jens Keim
Tim Grube
Shankar Karuppayah
Alejandro Sánchez Guinea
Max Mühlhäuser
186
17
0
05 Nov 2021
Universal Private Estimators
ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), 2021
Wei Dong
K. Yi
318
22
0
04 Nov 2021
Dynamic Differential-Privacy Preserving SGD
Jian Du
Song Li
Xiangyi Chen
Siheng Chen
Mingyi Hong
184
41
0
30 Oct 2021
Federated Learning with Heterogeneous Differential Privacy
Nasser Aldaghri
Hessam Mahdavifar
Ahmad Beirami
FedML
276
4
0
28 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
International Conference on Machine Learning (ICML), 2021
Paul Mangold
A. Bellet
Joseph Salmon
Marc Tommasi
495
16
0
22 Oct 2021
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
Anda Cheng
Jiaxing Wang
Xi Sheryl Zhang
Qiang Chen
Peisong Wang
Jian Cheng
274
33
0
16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization
Xiaoxia Wu
Lingxiao Wang
Irina Cristali
Quanquan Gu
Rebecca Willett
340
6
0
14 Oct 2021
Communication-Efficient Triangle Counting under Local Differential Privacy
Jacob Imola
Takao Murakami
Kamalika Chaudhuri
362
41
0
13 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning
Neural Information Processing Systems (NeurIPS), 2021
Naman Agarwal
Peter Kairouz
Ziyu Liu
FedML
302
146
0
11 Oct 2021
Selective Differential Privacy for Language Modeling
North American Chapter of the Association for Computational Linguistics (NAACL), 2021
Weiyan Shi
Aiqi Cui
Evan Li
R. Jia
Zhou Yu
293
83
0
30 Aug 2021
Efficient Hyperparameter Optimization for Differentially Private Deep Learning
Aman Priyanshu
Rakshit Naidu
Fatemehsadat Mireshghallah
Mohammad Malekzadeh
185
9
0
09 Aug 2021
Large-Scale Differentially Private BERT
Rohan Anil
Badih Ghazi
Vineet Gupta
Ravi Kumar
Pasin Manurangsi
239
148
0
03 Aug 2021
An Efficient DP-SGD Mechanism for Large Scale NLP Models
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021
Christophe Dupuy
Radhika Arava
Rahul Gupta
Anna Rumshisky
SyDa
271
48
0
14 Jul 2021
Improving the Algorithm of Deep Learning with Differential Privacy
Mehdi Amian
84
1
0
12 Jul 2021
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
International Conference on Machine Learning (ICML), 2021
Xinwei Zhang
Xiangyi Chen
Min-Fong Hong
Zhiwei Steven Wu
Jinfeng Yi
FedML
213
120
0
25 Jun 2021
On Large-Cohort Training for Federated Learning
Neural Information Processing Systems (NeurIPS), 2021
Zachary B. Charles
Zachary Garrett
Zhouyuan Huo
Sergei Shmulyian
Virginia Smith
FedML
225
117
0
15 Jun 2021
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates
Rudrajit Das
Abolfazl Hashemi
Sujay Sanghavi
Inderjit S. Dhillon
FedML
157
4
0
13 Jun 2021
Instance-optimal Mean Estimation Under Differential Privacy
Neural Information Processing Systems (NeurIPS), 2021
Ziyue Huang
Yuting Liang
K. Yi
186
65
0
01 Jun 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Conference on Computer and Communications Security (CCS), 2021
Wei Ping
Fan Wu
Yunhui Long
Luka Rimanic
Ce Zhang
Yue Liu
FedML
690
73
0
20 Mar 2021
Quantifying identifiability to choose and audit
ε
ε
ε
in differentially private deep learning
Proceedings of the VLDB Endowment (PVLDB), 2021
Daniel Bernau
Günther Eibl
Philip-William Grassal
Hannah Keller
Florian Kerschbaum
FedML
145
7
0
04 Mar 2021
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Proceedings on Privacy Enhancing Technologies (PoPETs), 2021
Wenxiao Wang
Tianhao Wang
Lun Wang
Nanqing Luo
Pan Zhou
Basel Alomair
R. Jia
244
18
0
02 Mar 2021
Wide Network Learning with Differential Privacy
Huanyu Zhang
Ilya Mironov
Meisam Hejazinia
229
28
0
01 Mar 2021
Label Leakage and Protection in Two-party Split Learning
International Conference on Learning Representations (ICLR), 2021
Oscar Li
Jiankai Sun
Xin Yang
Weihao Gao
Hongyi Zhang
Junyuan Xie
Virginia Smith
Chong-Jun Wang
FedML
318
162
0
17 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
International Conference on Machine Learning (ICML), 2021
Peter Kairouz
Ziyu Liu
Thomas Steinke
FedML
437
279
0
12 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Neural Information Processing Systems (NeurIPS), 2021
Zhiqi Bu
Sivakanth Gopi
Janardhan Kulkarni
Y. Lee
J. Shen
U. Tantipongpipat
FedML
284
47
0
05 Feb 2021
Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent
Conference on Fairness, Accountability and Transparency (FAccT), 2021
Junyuan Hong
Zinan Lin
Jiayu Zhou
184
11
0
19 Jan 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu
Han Yu
Jiabo He
Chen Chen
Lichao Sun
Jun Zhao
Qiang Yang
Philip S. Yu
FedML
600
476
0
07 Dec 2020
Locally Differentially Private Analysis of Graph Statistics
USENIX Security Symposium (USENIX Security), 2020
Jacob Imola
Takao Murakami
Kamalika Chaudhuri
380
125
0
17 Oct 2020
Differentially Private Deep Learning with Direct Feedback Alignment
Jaewoo Lee
Daniel Kifer
FedML
82
9
0
08 Oct 2020
Improved Analysis of Clipping Algorithms for Non-convex Optimization
Neural Information Processing Systems (NeurIPS), 2020
Bohang Zhang
Jikai Jin
Cong Fang
Liwei Wang
321
111
0
05 Oct 2020
Training Production Language Models without Memorizing User Data
Swaroop Indra Ramaswamy
Om Thakkar
Rajiv Mathews
Galen Andrew
H. B. McMahan
Franccoise Beaufays
FedML
266
95
0
21 Sep 2020
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping
Proceedings on Privacy Enhancing Technologies (PoPETs), 2020
Jaewoo Lee
Daniel Kifer
286
68
0
07 Sep 2020
Fast Dimension Independent Private AdaGrad on Publicly Estimated Subspaces
Peter Kairouz
Mónica Ribero
Keith Rush
Abhradeep Thakurta
295
14
0
14 Aug 2020
Privacy Amplification via Random Check-Ins
Neural Information Processing Systems (NeurIPS), 2020
Borja Balle
Peter Kairouz
H. B. McMahan
Om Thakkar
Abhradeep Thakurta
FedML
275
78
0
13 Jul 2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh
Farzan Farnia
Ramtin Pedarsani
Ali Jadbabaie
FedML
OOD
265
181
0
16 Jun 2020
Understanding Unintended Memorization in Federated Learning
Om Thakkar
Swaroop Indra Ramaswamy
Rajiv Mathews
Franccoise Beaufays
FedML
264
49
0
12 Jun 2020
Differentially Private Stochastic Coordinate Descent
AAAI Conference on Artificial Intelligence (AAAI), 2020
Georgios Damaskinos
Celestine Mendler-Dünner
R. Guerraoui
N. Papandreou
Thomas Parnell
199
15
0
12 Jun 2020
Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent
Shuang Song
Thomas Steinke
Om Thakkar
Abhradeep Thakurta
195
51
0
11 Jun 2020
Anonymizing Data for Privacy-Preserving Federated Learning
Olivia Choudhury
A. Gkoulalas-Divanis
Theodoros Salonidis
I. Sylla
Yoonyoung Park
Grace Hsu
Amar K. Das
FedML
93
56
0
21 Feb 2020
Attack-Resistant Federated Learning with Residual-based Reweighting
Shuhao Fu
Chulin Xie
Yue Liu
Qifeng Chen
FedML
AAML
285
103
0
24 Dec 2019
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Yanan Li
Shusen Yang
Xuebin Ren
Cong Zhao
FedML
169
42
0
17 Dec 2019
Advances and Open Problems in Federated Learning
Peter Kairouz
H. B. McMahan
Brendan Avent
A. Bellet
M. Bennis
...
Zheng Xu
Qiang Yang
Felix X. Yu
Han Yu
Sen Zhao
FedML
AI4CE
617
7,525
0
10 Dec 2019
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
U. Tantipongpipat
Chris Waites
Digvijay Boob
Amaresh Ankit Siva
Rachel Cummings
SyDa
309
31
0
06 Dec 2019
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