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Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance.
Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Copycat cnn: Stealing knowledge by persuading confession with random non-labeled data
Jacson Rodrigues Correia-Silva, Rodrigo F Berriel, Claudine Badue, Alberto F de Souza, and Thiago Oliveira-Santos · 2018
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Entity resolution and federated learning get a federated resolution
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2018
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Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B Lee · 2019
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Learning privately over distributed features: An admm sharing approach
Yaochen Hu, Peng Liu, Linglong Kong, and Di Niu · 2019
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Prada: protecting against dnn model stealing attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan · 2019
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A communication efficient collaborative learning framework for distributed features
Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Prediction poisoning: Towards defenses against dnn model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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A framework for the extraction of deep neural networks by leveraging public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy · 2019
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A quasi-newton method based vertical federated learning framework for logistic regression
Kai Yang, Tao Fan, Tianjian Chen, Yuanming Shi, and Qiang Yang · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Deep learning with label differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
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Cafe: Catastrophic data leakage in vertical federated learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen · 2021
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Label leakage and protection in two-party split learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang · 2021
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Fate: An industrial grade platform for collaborative learning with data protection
Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, and Qiang Yang · 2021
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Defending label inference and backdoor attacks in vertical federated learning
Yang Liu, Zhihao Yi, Yan Kang, Yuanqin He, Wenhan Liu, Tianyuan Zou, and Qiang Yang · 2021
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Cited alongside, same era.
Shengwen Yang, Bing Ren, Xuhui Zhou, and Liping Liu · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Club: A contrastive log-ratio upper bound of mutual information
Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin · 2020
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Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
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Fedmvt: Semi-supervised vertical federated learning with multiview training
Yan Kang, Yang Liu, and Tianjian Chen · 2020
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Federated forest
Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, and Yu Zheng · 2020
Cited alongside, same era.
Byzantine-resilient secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2020
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Feature inference attack on model predictions in vertical federated learning
Xinjian Luo, Yuncheng Wu, Xiaokui Xiao, and Beng Chin Ooi · 2021
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Ppfl: privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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Soteria: Provable defense against privacy leakage in federated learning from representation perspective
Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, and Yiran Chen · 2021
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Data-free model extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J Walls, and Nicolas Papernot · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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Secure bilevel asynchronous vertical federated learning with backward updating
Qingsong Zhang, Bin Gu, Cheng Deng, and Heng Huang · 2021
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Label inference attacks against vertical federated learning
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X Liu, and Ting Wang · 2022
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Label inference attacks against vertical federated learning
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, and Ting Wang · 2022
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Comprehensive analysis of privacy leakage in vertical federated learning during prediction
Xue Jiang, Xuebing Zhou, and Jens Grossklags · 2022
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Fedcvt: Semi-supervised vertical federated learning with cross-view training
Yan Kang, Yang Liu, and Xinle Liang · 2022
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Fedsea: A semi-asynchronous federated learning framework for extremely heterogeneous devices
Jingwei Sun, Ang Li, Lin Duan, Samiul Alam, Xuliang Deng, Xin Guo, Haiming Wang, Maria Gorlatova, Mi Zhang, Hai Li, et al · 2022
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