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Federated learning with differential privacy, i.e.
Federated learning of out-of-vocabulary words
Françoise Simone Beaufays, Mingqing Chen, Rajiv Mathews, and Tom Ouyang. 2019 · 1903
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Advances and open problems in federated learning
Peter Kairouz et al. 2019 · 1912
Earlier work this paper cites.
Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov. 2020 · 2002
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
Earlier work this paper cites.
Differential privacy
Cynthia Dwork. 2011 · 2011
Earlier work this paper cites.
Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima. 2012 · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederick P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Learning with privacy at scale
Differential Privacy Team Apple. 2017 · 2017
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Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Thakurta. 2017 · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017 · 2017
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
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Who needs words? Lexicon-free speech recognition
Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. 2019 · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
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Can you compare perplexity across different segmentations?
Sabrina J. Mielke. 2019 · 2019
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The effect of natural distribution shift on question answering models
John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt. 2020 · 2020
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Federated heavy hitters discovery with differential privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, and Wei Li. 2020 · 2020
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi. 2018 · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
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
Cited alongside, same era.
Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar. 2021 · 2021
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Private multi-task learning: Formulation and applications to federated learning
Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith. 2021 · 2021
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huggingface/tokenizers: Fast state-of-the-art tokenizers optimized for research and production
Huggingface. 2021 · 2021
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Kaggle stackoverflow data
Kaggle. 2021 · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021 · 2021
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