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Federated learning (FL) has emerged as an effective technique to co-training machine learning models without actually sharing data and leaking privacy.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
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Semi-supervised learning with ladder networks. In Advances in neural information processing systems . 3546–3554
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Practical secure aggregation for federated learning on user-held data
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Differentially private federated learning: A client level perspective
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Temporal ensembling for semi-supervised learning
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Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics . PMLR, 1273–1282
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Advances in neural information processing systems . 1195–1204
Antti Tarvainen and Harri Valpola. 2017 · 2017
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018 · 2018
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Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi. 2018 · 2018
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Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar. 2018 · 2018
Cited alongside, same era.
Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018 · 2018
Cited alongside, same era.
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, et al · 2019
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Federated learning for keyword spotting. In ICASSP . IEEE, 6341–6345
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. 2019 · 2019
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Cmfl: Mitigating communication overhead for federated learning. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 954–964
WANG Luping, WANG Wei, and LI Bo. 2019 · 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 · 2019
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Federated machine learning: Concept and applications
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Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
Cited alongside, same era.
Loadaboost: Loss-based adaboost federated machine learning on medical data
Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. 2018 · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018 · 2018
Cited alongside, same era.
Adversarial dropout for supervised and semi-supervised learning
Sungrae Park, Jun-Keon Park, Su-Jin Shin, and Il-Chul Moon. 2018 · 2018
Cited alongside, same era.
On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. 2018 · 2018
Cited alongside, same era.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Cited alongside, same era.
There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson. 2019 · 2019
Cited alongside, same era.
Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring. In ICLR
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel. 2019a
Cited in the paper.
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019a · 2019
Later among the works it cites.
Exploiting Unlabeled Data in Smart Cities using Federated Learning
Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed Abdallah, and Ala Al-Fuqaha. 2020 · 2020
Closest in time.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
Closest in time.
Robust Federated Learning via Collaborative Machine Teaching.. In AAAI . 4075–4082
Yufei Han and Xiangliang Zhang. 2020 · 2020
Closest in time.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020 · 2020
Closest in time.
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 · 2020
Closest in time.
Federated semi-supervised learning with inter-client consistency & disjoint learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang. 2021 · 2021
Closest in time.