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Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants.
Efficient spectral feature selection with minimum redundancy
Zheng Zhao, Lei Wang, and Huan Liu · 2010
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ℓ 2 , 1 \ell_{2,1} -norm regularized discriminative feature selection for unsupervised learning
Yi Yang, Heng Tao Shen, Zhigang Ma, Zi Huang, and Xiaofang Zhou · 2011
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Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition
Yinfu Feng, Jun Xiao, Yueting Zhuang, and Xiaoming Liu · 2012
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Unsupervised Feature Selection for Multi-View Data in Social Media
Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu · 2013
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A survey on multi-view learning
Chang Xu, Dacheng Tao, and Chao Xu · 2013
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Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
Chenping Hou, Feiping Nie, Xuelong Li, Dongyun Yi, and Yi Wu · 2014
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Unsupervised feature selection with adaptive structure learning
Liang Du and Yi-Dong Shen · 2015
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Large-scale multi-view spectral clustering via bipartite graph
Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang · 2015
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 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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Feature Selection: A Data Perspective
Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu · 2017
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
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A communication efficient vertical federated learning framework
Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, and Qiang Yang · 2019
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Measure contribution of participants in federated learning
Quan Wang, Xiaodong Dang, and Ziye Zhou · 2019
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A quasi-newton method based vertical federated learning framework for logistic regression
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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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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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
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Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu · 2019
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Shengwen Yang, Bing Ren, Xuhui Zhou, and Liping Liu · 2019
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