Differentially Private Learning with Adaptive Clipping

9 May 2019

Papers citing "Differentially Private Learning with Adaptive Clipping"

50 / 54 papers shown

Title
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Z. Zhu Y. 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 33 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 53 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 50 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 74 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 26 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 26 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 38 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 21 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 44 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
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy Zikai Zhang Rui Hu 20 11 0 07 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent Hadrien Hendrikx Paul Mangold A. Bellet 26 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 26 20 0 14 Jun 2023
Can Public Large Language Models Help Private Cross-device Federated Learning? Boxin Wang Yibo Zhang Yuan Cao Bo-wen Li H. B. McMahan Sewoong Oh Zheng Xu Manzil Zaheer FedML 18 37 0 20 May 2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping Bryn Elesedy Marcus Hutter 11 1 0 06 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 33 9 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 15 18 0 22 Jan 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 9 28 0 29 Nov 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 43 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 24 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 27 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 24 3 0 28 Oct 2022
FLAIR: Federated Learning Annotated Image Repository Congzheng Song Filip Granqvist Kunal Talwar FedML 16 27 0 18 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 22 17 0 21 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 20 51 0 16 Jun 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 22 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 17 59 0 16 Feb 2022
Private Adaptive Optimization with Side Information Tian Li Manzil Zaheer Sashank J. Reddi Virginia Smith 16 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 20 99 0 28 Jan 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 13 46 0 25 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 21 5 0 01 Dec 2021
Differentially private stochastic expectation propagation (DP-SEP) Margarita Vinaroz Mijung Park 17 1 0 25 Nov 2021
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting Joaquín Delgado Fernández Sergio Potenciano Menci Chul Min Lee Gilbert Fridgen 28 53 0 17 Nov 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 22 14 0 22 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 24 6 0 14 Oct 2021
Communication-Efficient Triangle Counting under Local Differential Privacy Jacob Imola Takao Murakami Kamalika Chaudhuri 32 29 0 13 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 11 121 0 11 Oct 2021
Selective Differential Privacy for Language Modeling Weiyan Shi Aiqi Cui Evan Li R. Jia Zhou Yu 13 68 0 30 Aug 2021
Efficient Hyperparameter Optimization for Differentially Private Deep Learning Aman Priyanshu Rakshit Naidu Fatemehsadat Mireshghallah Mohammad Malekzadeh 14 5 0 09 Aug 2021
Large-Scale Differentially Private BERT Rohan Anil Badih Ghazi Vineet Gupta Ravi Kumar Pasin Manurangsi 22 131 0 03 Aug 2021
An Efficient DP-SGD Mechanism for Large Scale NLP Models Christophe Dupuy Radhika Arava Rahul Gupta Anna Rumshisky SyDa 13 35 0 14 Jul 2021
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang Xiangyi Chen Min-Fong Hong Zhiwei Steven Wu Jinfeng Yi FedML 22 90 0 25 Jun 2021
On Large-Cohort Training for Federated Learning Zachary B. Charles Zachary Garrett Zhouyuan Huo Sergei Shmulyian Virginia Smith FedML 16 112 0 15 Jun 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Boxin Wang Fan Wu Yunhui Long Luka Rimanic Ce Zhang Bo-wen Li FedML 26 63 0 20 Mar 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 10 232 0 12 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 19 41 0 05 Feb 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 172 355 0 07 Dec 2020