Privacy Amplification via Compression: Achieving the Optimal
Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation

Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation

4 April 2023

Papers citing "Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation"

19 / 19 papers shown

Title
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 56 1 0 14 Mar 2025
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification Andy Dong Wei-Ning Chen Ayfer Özgür FedML 54 1 0 04 Mar 2025
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization Sudeep Salgia Nikola Pavlovic Yuejie Chi Qing Zhao 39 0 0 06 Jan 2025
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness Hojat Allah Salehi Md Jueal Mia S. Sandeep Pradhan M. Hadi Amini Farhad Shirani FedML 31 0 0 20 Sep 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 35 0 0 08 Aug 2024
Enhanced Privacy Bound for Shuffle Model with Personalized Privacy Yi-xiao Liu Yuhan Liu Li Xiong Yujie Gu Hong Chen FedML 37 0 0 25 Jul 2024
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation Sajani Vithana V. Cadambe Flavio du Pin Calmon Haewon Jeong FedML 42 1 0 03 Jul 2024
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 39 2 0 28 May 2024
Learning with User-Level Local Differential Privacy Puning Zhao Li Shen Rongfei Fan Qingming Li Huiwen Wu Jiafei Wu Zhe Liu 27 2 0 27 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 47 3 0 02 May 2024
Advances and Open Challenges in Federated Learning with Foundation Models Chao Ren Han Yu Hongyi Peng Xiaoli Tang Anran Li ... A. Tan Bo Zhao Xiaoxiao Li Zengxiang Li Qiang Yang FedML AIFin AI4CE 72 6 0 23 Apr 2024
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar Samson Zhou FedML 27 5 0 16 Apr 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 36 1 0 07 Mar 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
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications Grant Wilkins Sheng Di Jon C. Calhoun Zilinghan Li Kibaek Kim Robert Underwood Richard Mortier Franck Cappello FedML 37 2 0 20 Dec 2023
Compression with Exact Error Distribution for Federated Learning Mahmoud Hegazy Rémi Leluc Cheuk Ting Li Aymeric Dieuleveut FedML 13 9 0 31 Oct 2023
Multi-Message Shuffled Privacy in Federated Learning Antonious M. Girgis Suhas Diggavi FedML 25 8 0 22 Feb 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 23 2 0 19 Feb 2023
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018