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Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets.
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MixMatch: A Holistic Approach to Semi-Supervised Learning
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Federated Learning for Emoji Prediction in a Mobile Keyboard
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ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
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Federated Learning: Challenges, Methods, and Future Directions
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Self-training with Noisy Student improves ImageNet classification
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Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
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Semi-Supervised Learning by Entropy Minimization. In Proceedings of the 17th International Conference on Neural Information Processing Systems (Vancouver, British Columbia, Canada) (NIPS’04) . MIT Press, Cambridge, MA, USA, 529–536
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Training Keyword Spotting Models on Non-IID Data with Federated Learning
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Federated Semi-Supervised Learning with Inter-Client Consistency
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Federated Learning of User Authentication Models
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Introduction to Semi-Supervised Learning . Vol. 3
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FedSemi: An Adaptive Federated Semi-Supervised Learning Framework
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML , Vol. 3
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Audio Surveillance of Roads: A System for Detecting Anomalous Sounds
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Distilling the Knowledge in a Neural Network
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Building Machines That Learn and Think Like People
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SGDR: Stochastic Gradient Descent with Restarts
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Federated Learning: Strategies for Improving Communication Efficiency
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Federated Learning with Non-IID Data
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Contactless cardiac arrest detection using smart devices
Justin Chan, Thomas Rea, Shyamnath Gollakota, and Jacob E Sunshine. 2019 · 2019
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Federated Learning for Keyword Spotting
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Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, and Ian J. Goodfellow. 2019 · 2019
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Self-supervised audio representation learning for mobile devices
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H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017 · 2017
Cited alongside, same era.
Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
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Cooperative learning of audio and video models from self-supervised synchronization. In Proceedings of the 32nd International Conference on Neural Information Processing Systems . 7774–7785
Bruno Korbar, Du Tran, and Lorenzo Torresani. 2018 · 2018
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Bat detective—Deep learning tools for bat acoustic signal detection
Oisin Mac Aodha, Rory Gibb, Kate E. Barlow, Ella Browning, Michael Firman, Robin Freeman, Briana Harder, Libby Kinsey, Gary R. Mead, Stuart E. Newson, Ivan Pandourski, Stuart Parsons, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark Girolami, Gabriel Brostow, and Kate E. Jones. 2018 · 2018
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Voxforge
Ken MacLean. 2018 · 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 · 2018
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Dan Stowell, Yannis Stylianou, Mike Wood, Hanna Pamula, and Hervé Glotin. 2018 · 2018
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Antti Tarvainen and Harri Valpola. 2018 · 2018
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Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry, and Dominik Roblek. 2019 · 2019
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A survey on semi-supervised learning
Jesper E. van Engelen and H. Hoos. 2019 · 2019
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Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In 2020 International Joint Conference on Neural Networks (IJCNN) . 1–8
Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness. 2020 · 2020
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Flower: A Friendly Federated Learning Research Framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane. 2020 · 2020
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FSD50k: an open dataset of human-labeled sound events
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra. 2020 · 2020
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Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
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Augmenting Conversational Agents with Ambient Acoustic Contexts. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Oldenburg, Germany) (MobileHCI ’20) . Association for Computing Machinery, New York, NY, USA, Article 33, 9 pages
Chunjong Park, Chulhong Min, Sourav Bhattacharya, and Fahim Kawsar. 2020 · 2020
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Towards federated unsupervised representation learning
Bram van Berlo, Aaqib Saeed, and Tanir Ozcelebi. 2020 · 2020
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End-to-End Speech Recognition from Federated Acoustic Models
Yan Gao, Titouan Parcollet, Javier Fernandez-Marques, Pedro P. B. de Gusmao, Daniel J. Beutel, and Nicholas D. Lane. 2021 · 2021
Closest in time.
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, Rafael G. L. D’Oliveira, Hubert Eichner, 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. 2021 · 2021
Closest in time.
Contrastive learning of general-purpose audio representations. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 3875–3879
Aaqib Saeed, David Grangier, and Neil Zeghidour. 2021 · 2021
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