Private Adaptive Optimization with Side Information

12 February 2022

Papers citing "Private Adaptive Optimization with Side Information"

28 / 28 papers shown

Title
Differentially Private Federated Learning With Time-Adaptive Privacy Spending Shahrzad Kiani Nupur Kulkarni Adam Dziedzic S. Draper Franziska Boenisch FedML Presented at ResearchTrend Connect \| FedML on 28 Mar 2025 145 0 0 25 Feb 2025
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight Tao Huang Qingyu Huang Xin Shi Jiayang Meng Guolong Zheng Xu Yang Xun Yi 26 0 0 05 Nov 2024
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning Hansi Yang James T. Kwok 56 0 0 19 Jun 2024
Delving into Differentially Private Transformer Youlong Ding Xueyang Wu Yining Meng Yonggang Luo Hao Wang Weike Pan 29 5 0 28 May 2024
Provable Privacy with Non-Private Pre-Processing Yaxian Hu Amartya Sanyal Bernhard Schölkopf 24 2 0 19 Mar 2024
On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? Shuqi Ke Charlie Hou Giulia Fanti Sewoong Oh 34 4 0 29 Feb 2024
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning Hideaki Takahashi SILM 25 2 0 29 Dec 2023
Private Learning with Public Features Walid Krichene Nicolas Mayoraz Steffen Rendle Shuang Song Abhradeep Thakurta Li Zhang 21 6 0 24 Oct 2023
Coupling public and private gradient provably helps optimization Ruixuan Liu Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 18 2 0 02 Oct 2023
Private Matrix Factorization with Public Item Features Mihaela Curmei Walid Krichene Li Zhang Mukund Sundararajan 26 3 0 17 Sep 2023
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging Meirui Jiang Yuan Zhong Anjie Le Xiaoxiao Li Qianming Dou FedML 37 5 0 24 Jul 2023
Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang Ashwinee Panda Vikash Sehwag Prateek Mittal 23 20 0 08 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 38 11 0 06 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zi-Han Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 30 19 0 23 May 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 21 37 0 20 May 2023
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing Yi Shi Kang Wei Li Shen Jun Li Xueqian Wang Bo Yuan Song Guo 33 5 0 02 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 94 167 0 01 Mar 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
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 30 15 0 01 Dec 2022
Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li Manzil Zaheer Ziyu Liu Sashank J. Reddi H. B. McMahan Virginia Smith 37 10 0 01 Dec 2022
Learning-Augmented Private Algorithms for Multiple Quantile Release M. Khodak Kareem Amin Travis Dick Sergei Vassilvitskii FedML 29 4 0 20 Oct 2022
Differentially Private Online-to-Batch for Smooth Losses Qinzi Zhang Hoang Tran Ashok Cutkosky FedML 38 4 0 12 Oct 2022
FLAIR: Federated Learning Annotated Image Repository Congzheng Song Filip Granqvist Kunal Talwar FedML 16 27 0 18 Jul 2022
Membership Inference Attack Using Self Influence Functions Gilad Cohen Raja Giryes TDI 25 12 0 26 May 2022
Mixed Federated Learning: Joint Decentralized and Centralized Learning S. Augenstein Andrew Straiton Hard Lin Ning K. Singhal Satyen Kale Kurt Partridge Rajiv Mathews FedML 17 8 0 26 May 2022
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 8 48 0 01 Dec 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 411 0 14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 180 154 0 26 Feb 2021