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Differentially Private Learning with Adaptive Clipping

Neural Information Processing Systems (NeurIPS), 2019

9 May 2019

Papers citing "Differentially Private Learning with Adaptive Clipping"

50 / 201 papers shown

Title
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu FedML 227 9 0 02 Mar 2024
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization Ce Feng Parv Venkitasubramaniam 230 2 0 09 Feb 2024
Federated Learning Priorities Under the European Union Artificial Intelligence Act Herbert Woisetschläger Alexander Erben Bill Marino Shiqiang Wang Nicholas D. Lane R. Mayer Hans-Arno Jacobsen 236 23 0 05 Feb 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 537 38 0 09 Jan 2024
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach Mahtab Talaei Iman Izadi FedML 100 12 0 04 Jan 2024
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection Tuan Hoang Santu Rana Sunil R. Gupta Svetha Venkatesh BDL MU 234 35 0 07 Dec 2023
Making Translators Privacy-aware on the User's Side Ryoma Sato 157 2 0 07 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 251 2 0 06 Dec 2023
Differentially Private SGD Without Clipping Bias: An Error-Feedback ApproachInternational Conference on Learning Representations (ICLR), 2023 Xinwei Zhang Zhiqi Bu Zhiwei Steven Wu Mingyi Hong 225 10 0 24 Nov 2023
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and ReleaseProceedings of the VLDB Endowment (PVLDB), 2023 Jie Fu Qingqing Ye Haibo Hu Zhili Chen Lulu Wang Kuncan Wang Xun Ran 268 25 0 23 Nov 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$ -Differential PrivacyNeural Information Processing Systems (NeurIPS), 2023 Chendi Wang Buxin Su Jiayuan Ye Reza Shokri Weijie J. Su FedML 321 16 0 30 Oct 2023
On the accuracy and efficiency of group-wise clipping in differentially private optimization Zhiqi Bu Ruixuan Liu Yu Wang Sheng Zha George Karypis VLM 190 5 0 30 Oct 2023
DP-SGD with weight clipping Antoine Barczewski Jan Ramon 400 1 0 27 Oct 2023
FedFed: Feature Distillation against Data Heterogeneity in Federated LearningNeural Information Processing Systems (NeurIPS), 2023 Zhiqin Yang Yonggang Zhang Yuxiang Zheng Xinmei Tian Hao Peng Tongliang Liu Bo Han FedML 181 109 0 08 Oct 2023
Coupling public and private gradient provably helps optimization Ruixuan Liu Zhiqi Bu Yu Wang Sheng Zha George Karypis 223 3 0 02 Oct 2023
Online Sensitivity Optimization in Differentially Private LearningAAAI Conference on Artificial Intelligence (AAAI), 2023 Filippo Galli C. Palamidessi Tommaso Cucinotta 169 2 0 02 Oct 2023
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box MechanismsProceedings of the VLDB Endowment (PVLDB), 2023 Shweta Patwa Danyu Sun Amir Gilad Ashwin Machanavajjhala Sudeepa Roy 143 2 0 15 Sep 2023
Byzantine-Robust Federated Learning with Variance Reduction and Differential PrivacyIEEE Conference on Communications and Network Security (CNS), 2023 Zikai Zhang Rui Hu 179 13 0 07 Sep 2023
Advancing Personalized Federated Learning: Group Privacy, Fairness, and BeyondSN Computer Science (SCS), 2023 Filippo Galli Kangsoo Jung Sayan Biswas C. Palamidessi Tommaso Cucinotta FedML 207 16 0 01 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient DescentInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Aymeric Dieuleveut Paul Mangold A. Bellet 299 1 0 29 Aug 2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and FilteringIEEE Symposium on Security and Privacy (IEEE S&P), 2023 Ce Feng Nuo Xu Wujie Wen Parv Venkitasubramaniam Caiwen Ding 161 5 0 25 Jul 2023
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023 Meirui Jiang Yuan Zhong Anjie Le Xiaoxiao Li Qianming Dou FedML 313 6 0 24 Jul 2023
PLAN: Variance-Aware Private Mean EstimationProceedings on Privacy Enhancing Technologies (PoPETs), 2023 Martin Aumüller C. Lebeda Boel Nelson Rasmus Pagh FedML 240 5 0 14 Jun 2023
Differentially Private Wireless Federated Learning Using Orthogonal Sequences Xizixiang Wei Tianhao Wang Ruiquan Huang Cong Shen Jing Yang H. Vincent Poor 250 1 0 14 Jun 2023
Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization Zhigang Kan Linbo Qiao Hao Yu Liwen Peng Yifu Gao Dongsheng Li 224 26 0 14 Jun 2023
Differentially Private Sharpness-Aware TrainingInternational Conference on Machine Learning (ICML), 2023 Jinseong Park Hoki Kim Yujin Choi Jaewook Lee 219 11 0 09 Jun 2023
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing SystemsInternational Middleware Conference (Middleware), 2023 Herbert Woisetschläger Alexander Isenko R. Mayer Hans-Arno Jacobsen FedML 255 4 0 08 Jun 2023
FedVal: Different good or different bad in federated learning Viktor Valadi Xinchi Qiu Pedro Gusmão Nicholas D. Lane Mina Alibeigi FedML AAML 231 7 0 06 Jun 2023
A Lightweight Method for Tackling Unknown Participation Statistics in Federated AveragingInternational Conference on Learning Representations (ICLR), 2023 Maroun Touma Mingyue Ji FedML 297 0 0 06 Jun 2023
Survey of Trustworthy AI: A Meta Decision of AI Caesar Wu Yuan-Fang Li Pascal Bouvry 279 3 0 01 Jun 2023
Federated Learning of Gboard Language Models with Differential PrivacyAnnual Meeting of the Association for Computational Linguistics (ACL), 2023 Zheng Xu Yanxiang Zhang Galen Andrew Christopher A. Choquette-Choo Peter Kairouz H. B. McMahan Jesse Rosenstock Yuanbo Zhang FedML 449 100 0 29 May 2023
DP-SGD Without Clipping: The Lipschitz Neural Network WayInternational Conference on Learning Representations (ICLR), 2023 Louis Bethune Thomas Massena Thibaut Boissin Yannick Prudent Corentin Friedrich Franck Mamalet A. Bellet M. Serrurier David Vigouroux 310 11 0 25 May 2023
Evaluating Privacy Leakage in Split Learning Xinchi Qiu Ilias Leontiadis Luca Melis Alex Sablayrolles Pierre Stock 244 7 0 22 May 2023
Can Public Large Language Models Help Private Cross-device Federated Learning? Wei Ping Yibo Jacky Zhang Yuan Cao Yue Liu H. B. McMahan Sewoong Oh Zheng Xu Manzil Zaheer FedML 367 45 0 20 May 2023
Securing Distributed SGD against Gradient Leakage ThreatsIEEE Transactions on Parallel and Distributed Systems (TPDS), 2023 Wenqi Wei Ling Liu Jingya Zhou Ka-Ho Chow Yanzhao Wu FedML 164 28 0 10 May 2023
DPMLBench: Holistic Evaluation of Differentially Private Machine LearningConference on Computer and Communications Security (CCS), 2023 Chengkun Wei Ming-Hui Zhao Zhikun Zhang Min Chen Wenlong Meng Bodong Liu Yuan-shuo Fan Wenzhi Chen 349 17 0 10 May 2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential PrivacyIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 Yi Shi Kang Wei Li Shen Yingqi Liu Xueqian Wang Bo Yuan Dacheng Tao FedML 249 5 0 01 May 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward PhaseIEEE Internet of Things Journal (IEEE IoT J.), 2023 Chih-Hsun Lin Chia-Yi Hsu Chia-Mu Yu Yang Cao Chun-ying Huang 245 1 0 20 Apr 2023
Make Landscape Flatter in Differentially Private Federated LearningComputer Vision and Pattern Recognition (CVPR), 2023 Yi Shi Yingqi Liu Kang Wei Li Shen Xueqian Wang Dacheng Tao FedML 199 86 0 20 Mar 2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms Alekh Agarwal H. B. McMahan Zheng Xu FedML 271 2 0 17 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential PrivacyJournal of Artificial Intelligence Research (JAIR), 2023 Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 474 235 0 01 Mar 2023
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated LearningIEEE Transactions on Big Data (IEEE Trans. Big Data), 2023 Shenghui Li Edith C.H. Ngai Thiemo Voigt FedML AAML 203 84 0 14 Feb 2023
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataInternational Conference on Learning Representations (ICLR), 2023 M. Crawshaw Yajie Bao Mingrui Liu FedML 181 10 0 14 Feb 2023
One-Shot Federated Conformal PredictionInternational Conference on Machine Learning (ICML), 2023 Pierre Humbert B. L. Bars A. Bellet Sylvain Arlot FedML 229 22 0 13 Feb 2023
One-shot Empirical Privacy Estimation for Federated LearningInternational Conference on Learning Representations (ICLR), 2023 Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith Suriyakumar FedML 694 43 0 06 Feb 2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping Bryn Elesedy Marcus Hutter 163 2 0 06 Feb 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential PrivacyNeural Information Processing Systems (NeurIPS), 2023 Anastasia Koloskova Ryan McKenna Zachary B. Charles Keith Rush Brendan McMahan 513 14 0 02 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew Paverd Shruti Tople Santiago Zanella Béguelin Richard Turner Antti Honkela 364 16 0 02 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 227 10 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future DirectionsFindings (Findings), 2023 Lijie Hu Ivan Habernal Lei Shen Haiyan Zhao AAML 236 23 0 22 Jan 2023