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The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data.
Ako: Decentralised deep learning with partial gradient exchange
Pijika Watcharapichat, Victoria Lopez Morales, Raul Fernandez, and Peter R Pietzuch · 1908
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Gossip-based ad hoc routing
Zygmunt J Haas, Joseph Y Halpern, and Li Li · 2002
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Bandwidth optimal all-reduce algorithms for clusters of workstations
Pitch Patarasuk and Xin Yuan · 2009
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A peer-to-peer recommender system for self-emerging user communities based on gossip overlays
Ranieri Baraglia, Patrizio Dazzi, Matteo Mordacchini, and Laura Ricci · 2013
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A reliable effective terascale linear learning system
Alekh Agarwal, Oliveier Chapelle, Miroslav Dudik, and John Langford · 2014
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Boryiing Su · 2014
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konecny, H Brendan Mcmahan, and Daniel Ramage · 2015
Cited alongside, same era.
Malt: distributed data-parallelism for existing ml applications
Hao Li, Asim Kadav, Erik Kruus, and Cristian Ungureanu · 2015
Cited alongside, same era.
Global analytics in the face of bandwidth and regulatory constraints
Ashish Vulimiri, Carlo Curino, Brighten Godfrey, Thomas Jungblut, Jitendra Padhye, and George Varghese · 2015
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Gossip training for deep learning
Michael Blot, David Picard, Matthieu Cord, and Nicolas Thome · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Federated learning: Strategies for improving communication efficiency
Jakub Konecny, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Sparknet: Training deep networks in spark
Philipp Moritz, Robert Nishihara, Ion Stoica, and Michael I Jordan · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Gossipgrad: Scalable deep learning using gossip communication based asynchronous gradient descent
Jeffrey A Daily, Abhinav Vishnu, Charles Siegel, Thomas Warfel, and Vinay C Amatya · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Mingwei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Cited alongside, same era.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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