Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

7 July 2020

Papers citing "Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification"

33 / 33 papers shown

Title
Differentially Private 2D Human Pose Estimation Kaushik Bhargav Sivangi Idris Zakariyya Paul Henderson F. Deligianni 143 0 0 14 Apr 2025
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi Yaoliang Yu Yang Cao FedML 88 5 0 17 Feb 2025
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
Does SGD really happen in tiny subspaces? Minhak Song Kwangjun Ahn Chulhee Yun 71 4 1 25 May 2024
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization Shun Takagi Li Xiong Fumiyuki Kato Yang Cao Masatoshi Yoshikawa 3DH 46 2 0 13 May 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
Differentially Private Non-convex Learning for Multi-layer Neural Networks Hanpu Shen Cheng-Long Wang Zihang Xiang Yiming Ying Di Wang 46 7 0 12 Oct 2023
Private Matrix Factorization with Public Item Features Mihaela Curmei Walid Krichene Li Zhang Mukund Sundararajan 34 3 0 17 Sep 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 42 11 0 11 Aug 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 46 11 0 06 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 35 19 0 23 May 2023
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy Kang Wei Jun Li Chuan Ma Ming Ding Feng Shu Haitao Zhao Wen Chen Hongbo Zhu FedML 30 4 0 09 Apr 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning Edwige Cyffers A. Bellet D. Basu FedML 29 5 0 24 Feb 2023
Subspace based Federated Unlearning Guang-Ming Li Li Shen Yan Sun Yuejun Hu Han Hu Dacheng Tao MU FedML 28 20 0 24 Feb 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 27 46 0 03 Dec 2022
Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li Manzil Zaheer Ziyu Liu Sashank J. Reddi H. B. McMahan Virginia Smith 42 10 0 01 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 22 0 0 14 Nov 2022
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 36 28 0 16 Aug 2022
Private Adaptive Optimization with Side Information Tian Li Manzil Zaheer Sashank J. Reddi Virginia Smith 37 35 0 12 Feb 2022
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics A. Banerjee Tiancong Chen Xinyan Li Yingxue Zhou 34 8 0 09 Jan 2022
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 26 5 0 01 Dec 2021
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith M. Suriyakumar Om Thakkar Abhradeep Thakurta 13 49 0 01 Dec 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 11 27 0 16 Oct 2021
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 347 0 13 Oct 2021
Efficient and Private Federated Learning with Partially Trainable Networks Hakim Sidahmed Zheng Xu Ankush Garg Yuan Cao Mingqing Chen FedML 49 13 0 06 Oct 2021
Private Adaptive Gradient Methods for Convex Optimization Hilal Asi John C. Duchi Alireza Fallah O. Javidbakht Kunal Talwar 11 53 0 25 Jun 2021
Privately Learning Subspaces Vikrant Singhal Thomas Steinke 19 20 0 28 May 2021
The Power of Sampling: Dimension-free Risk Bounds in Private ERM Yin Tat Lee Daogao Liu Zhou Lu 16 3 0 28 May 2021
Experiments with Rich Regime Training for Deep Learning Xinyan Li A. Banerjee 32 2 0 26 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 26 41 0 05 Feb 2021
Fast Dimension Independent Private AdaGrad on Publicly Estimated Subspaces Peter Kairouz Mónica Ribero Keith Rush Abhradeep Thakurta 85 14 0 14 Aug 2020