Federated Learning with Reduced Information Leakage and Computation

10 October 2023

Tongxin Yin

Xueru Zhang

Mohammad Mahdi Khalili

Mingyan Liu

FedML

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Papers citing "Federated Learning with Reduced Information Leakage and Computation"

24 / 24 papers shown

Title
Privacy-Aware Randomized Quantization via Linear Programming Zhongteng Cai Xueru Zhang Mohammad Mahdi Khalili 126 2 0 01 Jun 2024
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 73 274 0 30 Nov 2021
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 71 776 0 08 Nov 2021
Anarchic Federated Learning Haibo Yang Xin Zhang Prashant Khanduri Jia Liu FedML 47 58 0 23 Aug 2021
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy Jingchen Hu Joerg Drechsler Hang J Kim FedML 44 2 0 17 Mar 2021
Federated $f$ -Differential Privacy Qinqing Zheng Shuxiao Chen Qi Long Weijie J. Su FedML 118 55 0 22 Feb 2021
Robust and Differentially Private Mean Estimation Xiyang Liu Weihao Kong Sham Kakade Sewoong Oh OOD FedML 76 76 0 18 Feb 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 163 120 0 09 Feb 2021
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 68 1,337 0 15 Jul 2020
LDP-Fed: Federated Learning with Local Differential Privacy Stacey Truex Ling Liu Ka-Ho Chow Mehmet Emre Gursoy Wenqi Wei FedML 59 395 0 05 Jun 2020
FedSplit: An algorithmic framework for fast federated optimization Reese Pathak Martin J. Wainwright FedML 217 184 0 11 May 2020
Local Differential Privacy based Federated Learning for Internet of Things Yang Zhao Jun Zhao Mengmeng Yang Teng Wang Ning Wang Lingjuan Lyu Dusit Niyato Kwok-Yan Lam 62 300 0 19 Apr 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 179 1,437 0 29 Feb 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
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 143 1,157 0 13 Sep 2019
Tighter Theory for Local SGD on Identical and Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik 74 434 0 10 Sep 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 149 1,422 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 186 427 0 29 Nov 2018
Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms Xueru Zhang Mohammad Mahdi Khalili M. Liu 71 30 0 07 Oct 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 85 398 0 31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 84 389 0 04 Jul 2018
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model Brendan Avent Aleksandra Korolova David Zeber Torgeir Hovden B. Livshits FedML 60 100 0 02 May 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 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