The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

12 February 2021

Papers citing "The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation"

41 / 141 papers shown

Title
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen Christopher A. Choquette-Choo Peter Kairouz A. Suresh FedML 31 63 0 07 Mar 2022
Differentially Private Federated Learning with Local Regularization and Sparsification Anda Cheng Peisong Wang Xi Sheryl Zhang Jian Cheng FedML 20 70 0 07 Mar 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 19 62 0 15 Feb 2022
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification Fumiyuki Kato Yang Cao Masatoshi Yoshikawa FedML 19 5 0 15 Feb 2022
Shuffle Private Linear Contextual Bandits Sayak Ray Chowdhury Xingyu Zhou FedML 16 25 0 11 Feb 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H. Fowl Jonas Geiping Steven Reich Yuxin Wen Wojtek Czaja Micah Goldblum Tom Goldstein FedML 71 56 0 29 Jan 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 29 211 0 20 Jan 2022
Secret Sharing Sharing For Highly Scalable Secure Aggregation Timothy Stevens Joseph P. Near Christian Skalka FedML 14 5 0 03 Jan 2022
EIFFeL: Ensuring Integrity for Federated Learning A. Chowdhury Chuan Guo S. Jha L. V. D. van der Maaten FedML 77 73 0 23 Dec 2021
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 12 9 0 19 Dec 2021
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors Timothy Stevens Christian Skalka C. Vincent J. Ring Samuel Clark Joseph P. Near FedML 14 71 0 13 Dec 2021
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems Jiankai Jin Eleanor McMurtry Benjamin I. P. Rubinstein O. Ohrimenko 12 36 0 10 Dec 2021
Locally Differentially Private Sparse Vector Aggregation Mingxun Zhou Tianhao Wang T-H. Hubert Chan Giulia Fanti E. Shi FedML 37 28 0 07 Dec 2021
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 69 181 0 06 Dec 2021
Eluding Secure Aggregation in Federated Learning via Model Inconsistency Dario Pasquini Danilo Francati G. Ateniese FedML 12 100 0 14 Nov 2021
DP-REC: Private & Communication-Efficient Federated Learning Aleksei Triastcyn M. Reisser Christos Louizos FedML 12 16 0 09 Nov 2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation Eugene Bagdasaryan Peter Kairouz S. Mellem Adria Gascon Kallista A. Bonawitz D. Estrin Marco Gruteser 14 28 0 03 Nov 2021
Infinitely Divisible Noise in the Low Privacy Regime Rasmus Pagh N. Stausholm FedML 25 2 0 13 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 11 121 0 11 Oct 2021
Private Multi-Task Learning: Formulation and Applications to Federated Learning Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 13 19 0 30 Aug 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 175 411 0 14 Jul 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 15 25 0 17 Jun 2021
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services Y. Saputra Diep N. Nguyen D. Hoang Viet Quoc Pham E. Dutkiewicz W. Hwang FedML 12 12 0 17 Jun 2021
FED- $χ^2$ : Privacy Preserving Federated Correlation Test Lun Wang Qi Pang Shuai Wang D. Song FedML 23 5 0 30 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 12 44 0 11 May 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 37 30 0 20 Apr 2021
Communication-Efficient Agnostic Federated Averaging Jae Hun Ro Mingqing Chen Rajiv Mathews M. Mohri A. Suresh FedML 17 17 0 06 Apr 2021
D3p -- A Python Package for Differentially-Private Probabilistic Programming Lukas Prediger Niki Loppi Samuel Kaski Antti Honkela 9 6 0 22 Mar 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Boxin Wang Fan Wu Yunhui Long Luka Rimanic Ce Zhang Bo-wen Li FedML 29 63 0 20 Mar 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
Auto-weighted Robust Federated Learning with Corrupted Data Sources Shenghui Li Edith C. H. Ngai Fanghua Ye Thiemo Voigt FedML 16 28 0 14 Jan 2021
FedEval: A Holistic Evaluation Framework for Federated Learning Di Chai Leye Wang Liu Yang Junxue Zhang Kai Chen Qian Yang FedML 25 8 0 19 Nov 2020
Privacy-preserving Data Sharing on Vertically Partitioned Data Razane Tajeddine Joonas Jälkö Samuel Kaski Antti Honkela FedML 28 8 0 19 Oct 2020
Strengthening Order Preserving Encryption with Differential Privacy Amrita Roy Chowdhury Bolin Ding S. Jha Weiran Liu Jingren Zhou 13 8 0 11 Sep 2020
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 13 18 0 12 Jun 2020
Device Heterogeneity in Federated Learning: A Superquantile Approach Yassine Laguel Krishna Pillutla J. Malick Zaïd Harchaoui FedML 32 22 0 25 Feb 2020
Robust Aggregation for Federated Learning Krishna Pillutla Sham Kakade Zaïd Harchaoui FedML 30 629 0 31 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 66 6,063 0 10 Dec 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 39 55 0 24 Sep 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 138 420 0 29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 85 278 0 02 Oct 2017