Federated Learning with Local Differential Privacy: Trade-offs between
Privacy, Utility, and Communication

Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication

9 February 2021

Rafael F. Schaefer

ArXiv (abs)PDF HTML

Papers citing "Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication"

17 / 17 papers shown

Title
A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues Zibo Wang Haichao Ji Yifei Zhu Dan Wang Zhu Han 99 1 0 19 Apr 2024
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 130 4 0 01 Aug 2023
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy Lichao Sun Jianwei Qian Xun Chen FedML 68 211 0 31 Jul 2020
LDP-Fed: Federated Learning with Local Differential Privacy Stacey Truex Ling Liu Ka-Ho Chow Mehmet Emre Gursoy Wenqi Wei FedML 56 395 0 05 Jun 2020
UVeQFed: Universal Vector Quantization for Federated Learning Nir Shlezinger Mingzhe Chen Yonina C. Eldar H. Vincent Poor Shuguang Cui FedML MQ 54 227 0 05 Jun 2020
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection Ruixuan Liu Yang Cao Masatoshi Yoshikawa Hong Chen FedML 45 108 0 24 Mar 2020
Wireless Federated Learning with Local Differential Privacy Mohamed Seif Ravi Tandon Ming Li 101 171 0 12 Feb 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 79 39 0 16 Jan 2020
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 125 1,616 0 01 Nov 2019
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 128 1,295 0 20 Dec 2017
Collecting Telemetry Data Privately Bolin Ding Janardhan Kulkarni Sergey Yekhanin 55 686 0 05 Dec 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 213 6,130 0 01 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016
The Composition Theorem for Differential Privacy Peter Kairouz Sewoong Oh Pramod Viswanath 118 681 0 04 Nov 2013
Privacy Aware Learning John C. Duchi Michael I. Jordan Martin J. Wainwright 153 290 0 07 Oct 2012
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 167 768 0 26 Sep 2011
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 134 1,487 0 01 Dec 2009