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Federated learning is a decentralized approach for training models on distributed devices, by summarizing local changes and sending aggregate parameters from local models to the cloud rather than the data itself.
A survey on neural network language models
Kun Jing and Jungang Xu. 2019 · 1906
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Daniel Ramage Seth Hampson Blaise Aguera y Arcas H. Brendan McMahan, Eider Moore. 2017 · 2017
Cited alongside, same era.
On the state of the art of evaluation in neural language models
Gábor Melis, Chris Dyer, and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
Federated learning for mobile keyboard prediction
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.
All-but-the-top: Simple and effective postprocessing for word representations
Jiaqi Mu and Pramod Viswanath. 2018 · 2018
Cited alongside, same era.
The Complete Works of William Shakespeare
William Shakespeare
Cited in the paper.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Effective dimensionality reduction for word embeddings
Vikas Raunak, Vivek Gupta, and Florian Metze. 2019 · 2019
Later among the works it cites.
Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan. 2020 · 2020
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