Quantification of the Leakage in Federated Learning

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Quantification of the Leakage in Federated Learning

12 October 2019

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Papers citing "Quantification of the Leakage in Federated Learning"

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Title
Trustworthy Federated Learning: Privacy, Security, and Beyond Chunlu Chen Ji Liu Haowen Tan Xingjian Li Kevin I-Kai Wang Peng Li Kouichi Sakurai Dejing Dou FedML 113 11 0 03 Nov 2024
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond Yuzheng Hu Tianle Cai Jinyong Shan Shange Tang Chaochao Cai Ethan Song Yue Liu Basel Alomair FedML AAML 80 10 0 19 Jul 2022
Collaborative Drug Discovery: Inference-level Data Protection Perspective Balázs Pejó Mina Remeli Adam Arany M. Galtier G. Ács 80 3 0 13 May 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 85 229 0 20 Jan 2022
From Distributed Machine Learning to Federated Learning: A Survey Ji Liu Jizhou Huang Yang Zhou Xuhong Li Shilei Ji Haoyi Xiong Dejing Dou FedML OOD 144 263 0 29 Apr 2021
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity Mathias Parisot Balázs Pejó Dayana Spagnuelo MIACV 149 34 0 27 Apr 2021
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation Lushan Song Guopeng Lin Jiaxuan Wang Haoqi Wu Wenqiang Ruan Weili Han 155 9 0 06 Dec 2020
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective Yi Liu Li Zhang Ning Ge Guanghao Li FedML 105 24 0 01 Dec 2020
Practical Privacy Attacks on Vertical Federated Learning Haiqin Weng Juntao Zhang Xingjun Ma Feng Xue Tao Wei S. Ji Zhiyuan Zong FedML 57 6 0 18 Nov 2020
Privacy Threats Against Federated Matrix Factorization Dashan Gao Ben Tan Ce Ju V. Zheng Qiang Yang 75 13 0 03 Jul 2020
Secure Federated Learning in 5G Mobile Networks Martin Isaksson K. Norrman 23 24 0 14 Apr 2020