Differentially Private Learning with Adaptive Clipping

9 May 2019

Papers citing "Differentially Private Learning with Adaptive Clipping"

50 / 67 papers shown

Title
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy Jiahao Xu Rui Hu Olivera Kotevska FedML 2 0 0 19 May 2025
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Zehan Zhu Yan Huang Xin Wang Shouling Ji Jinming Xu 26 0 0 10 May 2025
Spectral and Temporal Denoising for Differentially Private Optimization Hyeju Shin Kyudan Jung Seongwon Yun Juyoung Yun 38 0 0 07 May 2025
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation Chengkun Wei Weixian Li Chen Gong Wenzhi Chen 58 0 0 29 Mar 2025
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
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 78 2 0 04 Oct 2024
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis Herbert Woisetschläger Simon Mertel Christoph Krönke R. Mayer Hans-Arno Jacobsen FedML 41 2 0 11 Jul 2024
Releasing Large-Scale Human Mobility Histograms with Differential Privacy Christopher Bian Albert Cheu Yannis Guzman Marco Gruteser Peter Kairouz Ryan McKenna Edo Roth 33 0 0 03 Jul 2024
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning Yixuan Liu Li Xiong Yuhan Liu Yujie Gu Ruixuan Liu Hong Chen 40 1 0 04 Jun 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 28 15 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 47 18 0 09 Jan 2024
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance Haichao Sha Ruixuan Liu Yi-xiao Liu Hong Chen 52 1 0 06 Dec 2023
DP-SGD with weight clipping Antoine Barczewski Jan Ramon 11 1 0 27 Oct 2023
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy Zikai Zhang Rui Hu 41 11 0 07 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent Hadrien Hendrikx Paul Mangold A. Bellet 33 1 0 29 Aug 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 28 20 0 14 Jun 2023
Can Public Large Language Models Help Private Cross-device Federated Learning? Wei Ping Yibo Jacky Zhang Yuan Cao Bo-wen Li H. B. McMahan Sewoong Oh Zheng Xu Manzil Zaheer FedML 29 37 0 20 May 2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy Yi Shi Kang Wei Li Shen Yingqi Liu Xueqian Wang Bo Yuan Dacheng Tao FedML 41 2 0 01 May 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase Chih-Hsun Lin Chia-Yi Hsu Chia-Mu Yu Yang Cao Chun-ying Huang 36 1 0 20 Apr 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 96 167 0 01 Mar 2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping Bryn Elesedy Marcus Hutter 19 1 0 06 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 30 18 0 22 Jan 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 30 46 0 03 Dec 2022
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 25 28 0 29 Nov 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 2022
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 32 14 0 21 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 30 29 0 20 Nov 2022
Privacy-preserving Non-negative Matrix Factorization with Outliers Swapnil Saha H. Imtiaz PICV 21 3 0 02 Nov 2022
DPVIm: Differentially Private Variational Inference Improved Joonas Jälkö Lukas Prediger Antti Honkela Samuel Kaski 34 3 0 28 Oct 2022
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning Haibo Yang Pei-Yuan Qiu Jia Liu FedML 32 12 0 03 Oct 2022
FLAIR: Federated Learning Annotated Image Repository Congzheng Song Filip Granqvist Kunal Talwar FedML 26 28 0 18 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 26 17 0 21 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 22 52 0 16 Jun 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 29 25 0 15 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 27 69 0 14 Jun 2022
Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation Qian Chen Zilong Wang Jiawei Chen Haonan Yan Xiaodong Lin FedML 10 17 0 08 Jun 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 32 44 0 10 Mar 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams S. Denisov H. B. McMahan J. Rush Adam D. Smith Abhradeep Thakurta FedML 33 60 0 16 Feb 2022
Private Adaptive Optimization with Side Information Tian Li Manzil Zaheer Sashank J. Reddi Virginia Smith 37 35 0 12 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 36 100 0 28 Jan 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 27 46 0 25 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 30 5 0 01 Dec 2021
Differentially private stochastic expectation propagation (DP-SEP) Margarita Vinaroz Mijung Park 25 1 0 25 Nov 2021
Differentially Private Federated Learning on Heterogeneous Data Maxence Noble A. Bellet Aymeric Dieuleveut FedML 13 102 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 33 53 0 17 Nov 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 14 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 39 27 0 16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 38 6 0 14 Oct 2021