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To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to learn a common representation model without supervision while preserving data privacy.
Tian, Y.; Krishnan, D.; and Isola, P. 2019 · 1906
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Advances and open problems in federated learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2019 · 1912
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020a · 2002
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Wang, H.; Yurochkin, M.; Sun, Y.; Papailiopoulos, D.; and Khazaeni, Y. 2020 · 2002
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Wang, T.; and Isola, P. 2020 · 2005
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Hinton, G. E.; and Salakhutdinov, R. R. 2006 · 2006
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Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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An analysis of single-layer networks in unsupervised feature learning
Coates, A.; Ng, A.; and Lee, H. 2011 · 2011
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Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Kingma, D. P.; and Welling, M. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013 · 2013
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Dosovitskiy, A.; Springenberg, J. T.; Riedmiller, M.; and Brox, T. 2014 · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
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Jeong, E.; Oh, S.; Kim, H.; Park, J.; Bennis, M.; and Kim, S.-L. 2018 · 2018
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Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2018 · 2018
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An efficient framework for learning sentence representations
Logeswaran, L.; and Lee, H. 2018 · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
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Time-contrastive networks: Self-supervised learning from video
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He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Sohn, K. 2016 · 2016
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Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K.; Ivanov, V.; Kreuter, B.; Marcedone, A.; McMahan, H. B.; Patel, S.; Ramage, D.; Segal, A.; and Seth, K. 2017 · 2017
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Sattler, F.; Wiedemann, S.; Müller, K.-R.; and Samek, W. 2019 · 2019
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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