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Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Tutorial on variational autoencoders
Carl Doersch · 2016
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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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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 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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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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Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2019
Cited alongside, same era.
Federated semi-supervised learning with inter-client consistency & disjoint learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2020
Vf2boost: Very fast vertical federated gradient boosting for cross-enterprise learning
Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, and Bin Cui · 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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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 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.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Federated forest
Yang Liu, Yingting Liu, Zhijie Liu, Yuxuan Liang, Chuishi Meng, Junbo Zhang, and Yu Zheng · 2020
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
Cited alongside, same era.
Privacy preserving vertical federated learning for tree-based models
Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi · 2020
Cited alongside, same era.
Semi-supervised federated learning for activity recognition
Yuchen Zhao, Hanyang Liu, Honglin Li, Payam Barnaghi, and Hamed Haddadi · 2020
Cited alongside, same era.
Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, and Qiang Yang · 2021
Cited alongside, same era.
Semifl: Communication efficient semi-supervised federated learning with unlabeled clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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Pyvertical: A vertical federated learning framework for multi-headed splitnn
Daniele Romanini, Adam James Hall, Pavlos Papadopoulos, Tom Titcombe, Abbas Ismail, Tudor Cebere, Robert Sandmann, Robin Roehm, and Michael A Hoeh · 2021
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Federated semi-supervised learning for covid region segmentation in chest ct using multi-national data from china, italy, japan
Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, et al · 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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Improving semi-supervised federated learning by reducing the gradient diversity of models
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E Gonzalez, Kannan Ramchandran, and Michael W Mahoney · 2021
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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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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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Practical vertical federated learning with unsupervised representation learning
Zhaomin Wu, Qinbin Li, and Bingsheng He · 2022
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