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Vertical federated learning (VFL) enables multiple parties with disjoint features of a common user set to train a machine learning model without sharing their private data.
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Reliable writer identification in medieval manuscripts through page layout features: The “avila” bible case
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A communication efficient vertical federated learning framework
Yang Liu, Yan Kang, Liping Li, Xinwei Zhang, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang · 2019
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Federated learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2019
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Pivodl: Privacy-preserving vertical federated learning over distributed labels
Hangyu Zhu, Rui Wang, Yaochu Jin, and Kaitai Liang · 2021
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Improving robustness to model inversion attacks via mutual information regularization
Tianhao Wang, Yuheng Zhang, and Ruoxi Jia · 2021
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Deep learning with label differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, and Chiyuan Zhang · 2021
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Enabling sql-based training data debugging for federated learning
Yejia Liu, Weiyuan Wu, Lampros Flokas, Jiannan Wang, and Eugene Wu · 2021
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Sanjay Kariyappa and Moinuddin K Qureshi · 2021
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Fabio Mendoza Palechor and Alexis de la Hoz Manotas · 2019
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Privacy preserving vertical federated learning for tree-based models
Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi · 2020
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Fanglan Zheng, Kun Li, Jiang Tian, Xiaojia Xiang, et al · 2020
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Federated forest
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Federboost: Private federated learning for gbdt
Zhihua Tian, Rui Zhang, Xiaoyang Hou, Jian Liu, and Kui Ren · 2020
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The k-means algorithm: A comprehensive survey and performance evaluation
Mohiuddin Ahmed, Raihan Seraj, and Syed Mohammed Shamsul Islam · 2020
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Improving robustness to model inversion attacks via mutual information regularization
Tianhao Wang, Yuheng Zhang, and Ruoxi Jia · 2020
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Label leakage from gradients in distributed machine learning
Aidmar Wainakh, Till Müßig, Tim Grube, and Max Mühlhäuser · 2021
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Privacy-preserving training of tree ensembles over continuous data
Samuel Adams, Chaitali Choudhary, Martine De Cock, Rafael Dowsley, David Melanson, Anderson CA Nascimento, Davis Railsback, and Jianwei Shen · 2021
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Xorboost: Tree boosting in the multiparty computation setting
Kevin Deforth, Marc Desgroseilliers, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev, and Marius Vuille · 2021
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Large-scale secure xgb for vertical federated learning
Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, and Benyu Zhang · 2021
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Opboost: a vertical federated tree boosting framework based on order-preserving desensitization
Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin · 2022
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Vertical federated learning, 2022
Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, and Qiang Yang · 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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Label leakage and protection from forward embedding in vertical federated learning
Jiankai Sun, Xin Yang, Yuanshun Yao, and Chong Wang · 2022
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Revfrf: Enabling cross-domain random forest training with revocable federated learning
Yang Liu, Zhuo Ma, Yilong Yang, Ximeng Liu, Jianfeng Ma, and Kui Ren · 2022
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An efficient and robust system for vertically federated random forest
Houpu Yao, Jiazhou Wang, Peng Dai, Liefeng Bo, and Yanqing Chen · 2022
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Feverless: Fast and secure vertical federated learning based on xgboost for decentralized labels
Rui Wang, Oğuzhan Ersoy, Hangyu Zhu, Yaochu Jin, and Kaitai Liang · 2022
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Herb: Privacy-preserving random forest with partially homomorphic encryption
Qianying Liao, Bruno Cabral, João Paulo Fernandes, and Nuno Lourenço · 2022
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Residue-based label protection mechanisms in vertical logistic regression
Juntao Tan, Lan Zhang, Yang Liu, Anran Li, and Ye Wu · 2022
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Your labels are selling you out: Relation leaks in vertical federated learning
Pengyu Qiu, Xuhong Zhang, Shouling Ji, Tianyu Du, Yuwen Pu, Jun Zhou, and Ting Wang · 2022
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User-level label leakage from gradients in federated learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, and Max Mühlhäuser · 2022
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A survey on vertical federated learning: From a layered perspective
Liu Yang, Di Chai, Junxue Zhang, Yilun Jin, Leye Wang, Hao Liu, Han Tian, Qian Xu, and Kai Chen · 2023
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Vf-cart: A communication-efficient vertical federated framework for the cart algorithm
Yang Xu, Xuexian Hu, Jianghong Wei, Hongjian Yang, and Kejia Li · 2023
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