Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping

7 September 2020

Papers citing "Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping"

16 / 16 papers shown

Title
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 51 0 0 03 Feb 2025
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare Ming Li Pengcheng Xu Junjie Hu Zeyu Tang Guang Yang FedML 50 1 0 15 Sep 2024
Data Shapley in One Training Run Jiachen T. Wang Prateek Mittal Dawn Song Ruoxi Jia TDI 55 7 0 16 Jun 2024
Delving into Differentially Private Transformer Youlong Ding Xueyang Wu Yining Meng Yonggang Luo Hao Wang Weike Pan 44 5 0 28 May 2024
All Rivers Run to the Sea: Private Learning with Asymmetric Flows Yue Niu Ramy E. Ali Saurav Prakash Salman Avestimehr FedML 38 2 0 05 Dec 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 43 19 0 23 May 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 108 167 0 01 Mar 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 41 46 0 03 Dec 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 37 14 0 21 Nov 2022
Revisiting Hyperparameter Tuning with Differential Privacy Youlong Ding Xueyang Wu 24 0 0 03 Nov 2022
Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu Yu Wang Sheng Zha George Karypis 45 52 0 30 Sep 2022
Masked LARk: Masked Learning, Aggregation and Reporting worKflow Joseph J. Pfeiffer Denis Xavier Charles Davis Gilton Young Hun Jung Mehul Parsana Erik Anderson 33 11 0 27 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
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 168 350 0 25 Sep 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 44 41 0 05 Feb 2021
Efficient Per-Example Gradient Computations Ian Goodfellow 186 75 0 07 Oct 2015