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AdaCliP: Adaptive Clipping for Private SGD
20 August 2019
Venkatadheeraj Pichapati
A. Suresh
Felix X. Yu
Sashank J. Reddi
Sanjiv Kumar
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Papers citing
"AdaCliP: Adaptive Clipping for Private SGD"
28 / 78 papers shown
Universal Private Estimators
ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), 2021
Wei Dong
K. Yi
319
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
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
NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU
Edward H. Lee
M. M. Krell
Alexander Tsyplikhin
Victoria Rege
E. Colak
Kristen W. Yeom
FedML
163
0
0
24 Sep 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
Private Adaptive Gradient Methods for Convex Optimization
International Conference on Machine Learning (ICML), 2021
Hilal Asi
John C. Duchi
Alireza Fallah
O. Javidbakht
Kunal Talwar
214
60
0
25 Jun 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
Instance-optimal Mean Estimation Under Differential Privacy
Neural Information Processing Systems (NeurIPS), 2021
Ziyue Huang
Yuting Liang
K. Yi
187
65
0
01 Jun 2021
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Fan Mo
Anastasia Borovykh
Mohammad Malekzadeh
Soteris Demetriou
Deniz Gündüz
Hamed Haddadi
FedML
187
5
0
28 May 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
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
N-grams Bayesian Differential Privacy
Osman Ramadan
James Withers
Douglas Orr
105
0
0
29 Jan 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
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
IEEE Symposium on Security and Privacy (IEEE S&P), 2021
Milad Nasr
Shuang Song
Abhradeep Thakurta
Nicolas Papernot
Nicholas Carlini
MIACV
FedML
375
263
0
11 Jan 2021
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 and Fair Deep Learning: A Lagrangian Dual Approach
AAAI Conference on Artificial Intelligence (AAAI), 2020
Cuong Tran
Ferdinando Fioretto
Pascal Van Hentenryck
FedML
171
87
0
26 Sep 2020
Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning
Benjamin Zi Hao Zhao
M. Kâafar
N. Kourtellis
119
33
0
20 Aug 2020
Fast Dimension Independent Private AdaGrad on Publicly Estimated Subspaces
Peter Kairouz
Mónica Ribero
Keith Rush
Abhradeep Thakurta
295
14
0
14 Aug 2020
Differentially Private Accelerated Optimization Algorithms
Nurdan Kuru
cS. .Ilker Birbil
Mert Gurbuzbalaban
S. Yıldırım
144
26
0
05 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
On the effect of normalization layers on Differentially Private training of deep Neural networks
A. Davody
David Ifeoluwa Adelani
Thomas Kleinbauer
Dietrich Klakow
140
9
0
19 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
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
Deep Learning with Gaussian Differential Privacy
Harvard data science review (HDSR), 2019
Zhiqi Bu
Jinshuo Dong
Qi Long
Weijie J. Su
FedML
198
228
0
26 Nov 2019
Differentially Private Learning with Adaptive Clipping
Neural Information Processing Systems (NeurIPS), 2019
Galen Andrew
Om Thakkar
H. B. McMahan
Swaroop Ramaswamy
FedML
503
391
0
09 May 2019
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