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Federated learning is a distributed form of machine learning where both the training data and model training are decentralized.
“Long short-term memory,”
S. Hochreiter and J. Schmidhuber, · 1997
Earlier work this paper cites.
“Simple demographics often identify people uniquely,”
Latanya Sweeney, · 2000
Earlier work this paper cites.
“Differential privacy,”
Cynthia Dwork, · 2006
Earlier work this paper cites.
The Energy Access Situation in Developing Countries: A Review Focusing on the Least Developed Countries and Sub-Saharan Africa
G. Legros, World Health Organization, and United Nations Development Programme, · 2009
Earlier work this paper cites.
Online and upcoming: The Internet’s impact on India
Chandra Gnanasambandam, Anu Madgavkar, Noshir Kaka, James Manyika, Michael Chui, Jacques Bughin, and Malcolm Gomes, · 2012
Earlier work this paper cites.
“Federated learning: Strategies for improving communication efficiency,”
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon, · 2016
Earlier work this paper cites.
“Federated optimization: Distributed machine learning for on-device intelligence,”
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik, · 2016
Earlier work this paper cites.
“Deep learning with differential privacy,”
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, · 2016
Cited alongside, same era.
“Tensorflow: A system for large-scale machine learning,”
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, · 2016
Cited alongside, same era.
Mobile Internet Services in India Quality of Service
Udai S Mehta, Aaditeshwar Seth, Neha Tomar, and Rohit Singh, · 2016
Cited alongside, same era.
“Communication-efficient learning of deep networks from decentralized data,”
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas, · 2017
Cited alongside, same era.
“Federated multi-task learning,”
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar, · 2017
Cited alongside, same era.
“Federated learning: Collaborative machine learning without centralized training data,” https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Brendan McMahan and Daniel Ramage, · 2018
Closest in time.
“Graph oracle models, lower bounds, and gaps for parallel stochastic optimization,”
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nati Srebro, · 2018
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“How to backdoor federated learning,” 2018
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov, · 2018
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“Federated learning for mobile keyboard prediction,” 2018
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage, · 2018
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“cpsgd: Communication-efficient and differentially-private distributed sgd,”
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan, · 2018
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“Learning differentially private language models without losing accuracy,”
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang, · 2017
Cited alongside, same era.
“Practical secure aggregation for privacy-preserving machine learning,”
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth, · 2017
Cited alongside, same era.
“Tensorflow lite — tensorflow,” https://www.tensorflow.org/lite/
Google, · 2018
Closest in time.
“Google knowledge graph search api,” https://developers.google.com/knowledge-graph/
Google, · 2018
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