Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs

17 August 2020

Suhas Diggavi

Papers citing "Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs"

32 / 32 papers shown

Title
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 50 48 0 08 Jun 2021
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization Navjot Singh Deepesh Data Jemin George Suhas Diggavi 23 55 0 13 May 2020
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Shuang Song Kunal Talwar Abhradeep Thakurta 60 84 0 10 Jan 2020
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 126 6,177 0 10 Dec 2019
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization Navjot Singh Deepesh Data Jemin George Suhas Diggavi 33 23 0 31 Oct 2019
Improved Summation from Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 75 22 0 24 Sep 2019
Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova Tao R. Lin Sebastian U. Stich Martin Jaggi FedML 37 234 0 22 Jul 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 51 47 0 20 Jun 2019
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model Badih Ghazi Rasmus Pagh A. Velingker FedML 37 98 0 19 Jun 2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations Debraj Basu Deepesh Data C. Karakuş Suhas Diggavi MQ 38 403 0 06 Jun 2019
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters Jayadev Acharya Ziteng Sun 51 60 0 28 May 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 59 237 0 07 Mar 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 54 2,302 0 13 Feb 2019
Lower Bounds for Locally Private Estimation via Communication Complexity John C. Duchi Ryan M. Rogers 36 93 0 01 Feb 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes Sai Praneeth Karimireddy Quentin Rebjock Sebastian U. Stich Martin Jaggi 49 496 0 28 Jan 2019
Protection Against Reconstruction and Its Applications in Private Federated Learning Abhishek Bhowmick John C. Duchi Julien Freudiger Gaurav Kapoor Ryan M. Rogers FedML 50 358 0 03 Dec 2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 167 423 0 29 Nov 2018
The Convergence of Sparsified Gradient Methods Dan Alistarh Torsten Hoefler M. Johansson Sarit Khirirat Nikola Konstantinov Cédric Renggli 120 491 0 27 Sep 2018
Sparsified SGD with Memory Sebastian U. Stich Jean-Baptiste Cordonnier Martin Jaggi 66 743 0 20 Sep 2018
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 78 349 0 04 Aug 2018
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 111 490 0 27 May 2018
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication Jayadev Acharya Ziteng Sun Huanyu Zhang 40 148 0 13 Feb 2018
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 128 985 0 22 May 2017
Distributed Mean Estimation with Limited Communication A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 93 362 0 02 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 269 4,620 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 170 6,069 0 01 Jul 2016
Minimax Optimal Procedures for Locally Private Estimation John C. Duchi Martin J. Wainwright Michael I. Jordan 63 434 0 08 Apr 2016
Discrete Distribution Estimation under Local Privacy Peter Kairouz Kallista A. Bonawitz Daniel Ramage 27 328 0 24 Feb 2016
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 134 573 0 08 Dec 2012
Distributed Private Data Analysis: On Simultaneously Solving How and What A. Beimel Kobbi Nissim Eran Omri FedML 93 208 0 14 Mar 2011
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 93 1,482 0 01 Dec 2009
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 99 1,459 0 06 Mar 2008