FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses

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FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses

5 November 2024

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Papers citing "FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses"

16 / 16 papers shown

Title
Concealing Sensitive Samples against Gradient Leakage in Federated Learning Jing Wu Munawar Hayat Min Zhou Mehrtash Harandi FedML 35 10 0 13 Sep 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 62 13 0 12 Aug 2022
Data Leakage in Federated Averaging Dimitar I. Dimitrov Mislav Balunović Nikola Konstantinov Martin Vechev FedML 49 31 0 24 Jun 2022
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 126 148 0 25 Oct 2021
Towards General Deep Leakage in Federated Learning Jiahui Geng Yongli Mou Feifei Li Qing Li Oya Beyan Stefan Decker Chunming Rong FedML 59 58 0 18 Oct 2021
Dropout against Deep Leakage from Gradients Yanchong Zheng FedML 28 4 0 25 Aug 2021
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland MIACV 39 37 0 10 Aug 2021
See through Gradients: Image Batch Recovery via GradInversion Hongxu Yin Arun Mallya Arash Vahdat J. Álvarez Jan Kautz Pavlo Molchanov FedML 92 474 0 15 Apr 2021
Flower: A Friendly Federated Learning Research Framework Daniel J. Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques ... Lorenzo Sani Kwing Hei Li Titouan Parcollet Pedro Porto Buarque de Gusmão Nicholas D. Lane FedML 140 820 0 28 Jul 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 121 1,234 0 31 Mar 2020
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 259 1,785 0 18 Mar 2020
iDLG: Improved Deep Leakage from Gradients Bo Zhao Konda Reddy Mopuri Hakan Bilen FedML 81 643 0 08 Jan 2020
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion Hongxu Yin Pavlo Molchanov Zhizhong Li J. Álvarez Arun Mallya Derek Hoiem N. Jha Jan Kautz 84 569 0 18 Dec 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 103 2,227 0 21 Jun 2019
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 384 11,920 0 11 Jan 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 408 17,615 0 17 Feb 2016