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While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels.
Yang Chen, Xiaoyan Sun, and Yaochu Jin · 1903
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 1905
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Randaugment: Practical data augmentation with no separate search
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 1909
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 1911
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Asynchronous online federated learning for edge devices
Yujing Chen, Yue Ning, and Huzefa Rangwala · 1911
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Federated continual learning with weighted inter-client transfer
Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, and Sung Ju Hwang · 2003
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Self-ensembling for visual domain adaptation
Geoff French, Michal Mackiewicz, and Mark Fisher · 2018
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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One-shot federated learning
Neel Guha, Ameet Talwlkar, and Virginia Smith · 2019
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le · 2019
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
Later among the works it cites.
Exploiting unlabeled data in smart cities using federated learning
Abdullatif Albaseer, Bekir Ciftler, Mohamed Abdallah, and Ala Al-Fuqaha · 2020
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Can ai help in screening viral and covid-19 pneumonia?
Muhammad EH Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandaker Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al-Emadi, et al · 2020
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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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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel
Cited in the paper.
Scalable and order-robust continual learning with additive parameter decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang
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A survey towards federated semi-supervised learning
Yilun Jin, Xiguang Wei, Yang Liu, and Qiang Yang · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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