The Skellam Mechanism for Differentially Private Federated Learning

11 October 2021

Papers citing "The Skellam Mechanism for Differentially Private Federated Learning"

50 / 71 papers shown

Title
VDDP: Verifiable Distributed Differential Privacy under the Client-Server-Verifier Setup Haochen Sun Xi He 43 0 0 30 Apr 2025
$Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime$ Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime Charlie Harrison Pasin Manurangsi 26 0 0 07 Apr 2025
Random signed measures Riccardo Passeggeri 64 0 0 19 Nov 2024
Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning Marshall Ball James Bell-Clark Adria Gascon Peter Kairouz Sewoong Oh Zhiye Xie FedML 36 0 0 15 Oct 2024
Federated Learning in Practice: Reflections and Projections Katharine Daly Hubert Eichner Peter Kairouz H. B. McMahan Daniel Ramage Zheng Xu FedML 53 5 0 11 Oct 2024
Calibrating Noise for Group Privacy in Subsampled Mechanisms Yangfan Jiang Xinjian Luo Yin Yang Xiaokui Xiao 36 2 0 19 Aug 2024
Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning Joon Kim Sejin Park AAML FedML 40 1 0 15 Aug 2024
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong FedML 44 3 0 20 Jul 2024
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation Sajani Vithana V. Cadambe Flavio du Pin Calmon Haewon Jeong FedML 47 1 0 03 Jul 2024
Towards Efficient and Scalable Training of Differentially Private Deep Learning Sebastian Rodriguez Beltran Marlon Tobaben Niki Loppi Antti Honkela 34 0 0 25 Jun 2024
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs Charlie Hou Akshat Shrivastava Hongyuan Zhan Rylan Conway Trang Le Adithya Sagar Giulia Fanti Daniel Lazar 36 8 0 05 Jun 2024
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 41 1 0 01 Jun 2024
Privacy-Aware Randomized Quantization via Linear Programming Zhongteng Cai Xueru Zhang Mohammad Mahdi Khalili 46 2 0 01 Jun 2024
FedSheafHN: Personalized Federated Learning on Graph-structured Data Wenfei Liang Yanan Zhao Rui She Yiming Li Wee Peng Tay FedML 56 0 0 25 May 2024
Differentially Private Federated Learning without Noise Addition: When is it Possible? Jiang Zhang Konstantinos Psounis FedML 53 0 0 06 May 2024
The Privacy Power of Correlated Noise in Decentralized Learning Youssef Allouah Anastasia Koloskova Aymane El Firdoussi Martin Jaggi R. Guerraoui 31 4 0 02 May 2024
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy Wei-Ning Chen Berivan Isik Peter Kairouz Albert No Sewoong Oh Zheng Xu 57 3 0 02 May 2024
Confidential Federated Computations Hubert Eichner Daniel Ramage Kallista A. Bonawitz Dzmitry Huba Tiziano Santoro ... Albert Cheu Katharine Daly Adria Gascon Marco Gruteser Brendan McMahan 48 2 0 16 Apr 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 38 1 0 07 Mar 2024
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos Tianyi Zhang Yu Cao Dianbo Liu FedML 21 0 0 29 Feb 2024
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data Richeng Jin Yujie Gu Kai Yue Xiaofan He Zhaoyang Zhang Huaiyu Dai FedML 20 0 0 16 Feb 2024
Decomposable Submodular Maximization in Federated Setting Akbar Rafiey FedML 30 1 0 31 Jan 2024
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning Zhifeng Jiang Peng Ye Shiqi He Wei Wang Ruichuan Chen Bo Li 31 2 0 05 Jan 2024
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning Guangfeng Yan Tan Li Tian-Shing Lan Kui Wu Linqi Song 19 6 0 12 Dec 2023
FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting Meenatchi Sundaram Muthu Selva Annamalai Igor Bilogrevic Emiliano De Cristofaro 40 1 0 28 Nov 2023
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment Matt Gorbett Hossein Shirazi Indrakshi Ray FedML 36 2 0 08 Nov 2023
Federated Experiment Design under Distributed Differential Privacy Wei-Ning Chen Graham Cormode Akash Bharadwaj Peter Romov Ayfer Özgür FedML 31 2 0 07 Nov 2023
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy Fumiyuki Kato Li Xiong Shun Takagi Yang Cao Masatoshi Yoshikawa FedML 22 3 0 23 Aug 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 75 4 0 01 Aug 2023
Private Federated Learning with Autotuned Compression Enayat Ullah Christopher A. Choquette-Choo Peter Kairouz Sewoong Oh FedML 15 6 0 20 Jul 2023
Private Federated Learning in Gboard Yuanbo Zhang Daniel Ramage Zheng Xu Yanxiang Zhang Shumin Zhai Peter Kairouz FedML 30 7 0 26 Jun 2023
Randomized Quantization is All You Need for Differential Privacy in Federated Learning Yeojoon Youn Zihao Hu Juba Ziani Jacob D. Abernethy FedML 24 21 0 20 Jun 2023
FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking Junchuan Liang Rong Wang FedML 22 11 0 02 Jun 2023
Federated Learning of Gboard Language Models with Differential Privacy Zheng Xu Yanxiang Zhang Galen Andrew Christopher A. Choquette-Choo Peter Kairouz H. B. McMahan Jesse Rosenstock Yuanbo Zhang FedML 42 77 0 29 May 2023
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination Ming Hu Zhihao Yue Zhiwei Ling Cheng Chen Yihao Huang Xian Wei Xiang Lian Yang Liu Mingsong Chen FedML 19 8 0 18 May 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 36 21 0 15 Apr 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 28 25 0 04 Apr 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 96 167 0 01 Mar 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q. S. Quek H. Dai FedML 29 2 0 19 Feb 2023
Balancing Privacy Protection and Interpretability in Federated Learning Zhe Li Honglong Chen Zhichen Ni Huajie Shao FedML 16 8 0 16 Feb 2023
$z$ -SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning Zhiwei Tang Yanmeng Wang Tsung-Hui Chang FedML 21 14 0 06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 30 18 0 22 Jan 2023
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML 17 20 0 09 Jan 2023
Recent Advances on Federated Learning: A Systematic Survey Bingyan Liu Nuoyan Lv Yuanchun Guo Yawen Li FedML 60 78 0 03 Jan 2023
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Xuming Hu Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 31 18 0 08 Dec 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Federated Calibration and Evaluation of Binary Classifiers Graham Cormode Igor L. Markov FedML 33 4 0 22 Oct 2022
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 29 29 0 06 Oct 2022
Sparse Random Networks for Communication-Efficient Federated Learning Berivan Isik Francesco Pase Deniz Gunduz Tsachy Weissman M. Zorzi FedML 70 52 0 30 Sep 2022
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy Zhifeng Jiang Wei Wang Ruichuan Chen 43 6 0 26 Sep 2022