Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off

10 February 2024

Papers citing "Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off"

20 / 20 papers shown

Title
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning Zebang Shen Jiayuan Ye Anmin Kang Hamed Hassani Reza Shokri FedML 75 16 0 11 Sep 2023
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning Xinnan Yuan W. Ni Ming Ding Kang Wei Jun Li H. Vincent Poor FedML 57 41 0 07 Mar 2023
No Free Lunch Theorem for Security and Utility in Federated Learning Xiaojin Zhang Hanlin Gu Lixin Fan Kai Chen Qiang Yang FedML 48 64 0 11 Mar 2022
Differentially Private Federated Learning with Local Regularization and Sparsification Anda Cheng Peisong Wang Xi Sheryl Zhang Jian Cheng FedML 40 74 0 07 Mar 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning A. Bietti Chen-Yu Wei Miroslav Dudík John Langford Zhiwei Steven Wu FedML 74 46 0 10 Feb 2022
Gradient Inversion with Generative Image Prior Jinwoo Jeon Jaechang Kim Kangwook Lee Sewoong Oh Jungseul Ok 55 153 0 28 Oct 2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 112 147 0 25 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 270 365 0 25 Sep 2021
Learning with User-Level Privacy Daniel Levy Ziteng Sun Kareem Amin Satyen Kale Alex Kulesza M. Mohri A. Suresh FedML 51 90 0 23 Feb 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 150 119 0 09 Feb 2021
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning Mohammad Naseri Jamie Hayes Emiliano De Cristofaro FedML 56 145 0 08 Sep 2020
LDP-Fed: Federated Learning with Local Differential Privacy Stacey Truex Ling Liu Ka-Ho Chow Mehmet Emre Gursoy Wenqi Wei FedML 56 393 0 05 Jun 2020
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 92 2,200 0 21 Jun 2019
Federated Optimization in Heterogeneous Networks Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 173 5,168 0 14 Dec 2018
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 134 1,419 0 03 Dec 2018
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm Canyi Lu Jiashi Feng Yudong Chen Wen Liu Zhouchen Lin Shuicheng Yan 88 757 0 10 Apr 2018
Differentially Private Matrix Completion Revisited Prateek Jain Om Thakkar Abhradeep Thakurta FedML 52 34 0 28 Dec 2017
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization Canyi Lu Jiashi Feng Yudong Chen Wen Liu Zhouchen Lin Shuicheng Yan 50 485 0 14 Aug 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 191 6,113 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 380 17,453 0 17 Feb 2016