Title
Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation Abdelrahman Abouelenin M. Abdelrehim Raffy Fahim Amr Hendy Mohamed Afify 31 0 0 08 May 2025
Towards Trustworthy Federated Learning with Untrusted Participants Youssef Allouah R. Guerraoui John Stephan FedML 46 0 0 03 May 2025
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Rob Romijnders Stefanos Laskaridis Ali Shahin Shamsabadi Hamed Haddadi 57 0 0 25 Apr 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models Kyeongkook Seo Dong-Jun Han Jaejun Yoo 40 0 0 11 Mar 2025
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang W. Zhang Xinlei He Kaishun He Hong Xing 45 0 0 25 Feb 2025
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study Ayana Moshruba Ihsen Alouani Maryam Parsa AAML 46 3 0 24 Feb 2025
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning Raghav Singhal Kaustubh Ponkshe Rohit Vartak Lav R. Varshney Praneeth Vepakomma FedML 74 0 0 24 Feb 2025
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 91 0 0 30 Dec 2024
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 80 3 0 21 Dec 2024
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 85 0 0 02 Dec 2024
Attribute Inference Attacks for Federated Regression Tasks Francesco Diana Othmane Marfoq Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML 163 1 0 19 Nov 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 36 5 0 08 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 32 0 0 01 Oct 2024
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 37 2 0 19 Aug 2024
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models Deniz Daum Richard Osuala Anneliese Riess Georgios Kaissis Julia A. Schnabel Maxime Di Folco MedIm 48 0 0 23 Jul 2024
Noise-Aware Differentially Private Regression via Meta-Learning Ossi Raisa Stratis Markou Matthew Ashman W. Bruinsma Marlon Tobaben Antti Honkela Richard E. Turner 66 1 0 12 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
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 45 9 0 27 May 2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off Yuecheng Li Lele Fu Tong Wang Jian Lou Bin Chen Lei Yang Zibin Zheng Zibin Zheng Chuan Chen FedML 65 4 0 10 Feb 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 23 5 0 29 Jan 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
Federated learning with differential privacy and an untrusted aggregator Kunlong Liu Trinabh Gupta 37 0 0 17 Dec 2023
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings Jean Ogier du Terrail Quentin Klopfenstein Honghao Li Imke Mayer Nicolas Loiseau Mohammad Hallal Félix Balazard M. Andreux 16 2 0 28 Nov 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 29 7 0 24 Nov 2023
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy Zikai Zhang Rui Hu 28 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
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks Shijie Liu Andrew C. Cullen Paul Montague S. Erfani Benjamin I. P. Rubinstein AAML 18 3 0 15 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 30 13 0 27 Jul 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 32 23 0 20 Jul 2023
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection Eli Chien Wei-Ning Chen Chao Pan Pan Li Ayfer Özgür O. Milenkovic 25 12 0 12 Jul 2023
Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks Emilie Grégoire M. H. Chaudhary Sam Verboven 24 1 0 07 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 44 11 0 06 Jun 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zi-Han Lin Sivakanth Gopi Janardhan Kulkarni Harsha Nori Sergey Yekhanin 37 36 0 24 May 2023
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning Yumeki Goto Tomoya Matsumoto Hamada Rizk Naoto Yanai Hirozumi Yamaguchi 17 6 0 14 May 2023
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning Md. Adnan Arefeen Zhouyu Li M. Y. S. Uddin Anupam Das 27 0 0 13 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 25 4 0 09 Apr 2023
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning Charlie Hou Hongyuan Zhan Akshat Shrivastava Sida I. Wang S. Livshits Giulia Fanti Daniel Lazar FedML 24 15 0 17 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 8 14 0 06 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 13 7 0 06 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 32 17 0 03 Feb 2023
Privacy and Efficiency of Communications in Federated Split Learning Zongshun Zhang Andrea Pinto Valeria Turina Flavio Esposito I. Matta FedML 19 32 0 04 Jan 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 31 11 0 08 Dec 2022
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning Hongbin Liu Wenjie Qu Jinyuan Jia Neil Zhenqiang Gong SSL 28 6 0 06 Dec 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 17 0 0 14 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 22 34 0 07 Nov 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 19 0 0 03 Nov 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 27 2 0 28 Oct 2022
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe Xiang Yue Huseyin A. Inan Xuechen Li Girish Kumar Julia McAnallen Hoda Shajari Huan Sun David Levitan Robert Sim 36 79 0 25 Oct 2022
A General Framework for Auditing Differentially Private Machine Learning Fred Lu Joseph Munoz Maya Fuchs Tyler LeBlond Elliott Zaresky-Williams Edward Raff Francis Ferraro Brian Testa FedML 11 35 0 16 Oct 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 36 142 0 10 Oct 2022