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Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections.
Quantized incremental algorithms for distributed optimization
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Alex Krizhevsky · 2009
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012
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Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Nando de Freitas, et al · 2013
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Large-scale learning with less ram via randomization
Daniel Golovin, D. Sculley, H. Brendan McMahan, and Michael Young · 2013
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Project adam: Building an efficient and scalable deep learning training system
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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Communication-efficient distributed optimization using an approximate Newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Sketching as a tool for numerical linear algebra
David P. Woodruff · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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DiSCO: Distributed optimization for self-concordant empirical loss
Yuchen Zhang and Xiao Lin · 2015
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Conversational contextual cues: The case of personalization and history for response ranking
Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun-hsuan Sung, Brian Strope, and Ray Kurzweil · 2016
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QSGD: Randomized quantization for communication-optimal stochastic gradient descent
Dan Alistarh, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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AIDE: Fast and communication efficient distributed optimization
Sashank J Reddi, Jakub Konečný, Peter Richtárik, Barnabás Póczós, and Alex Smola · 2016
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Speedtest market report
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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
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Distributed optimization with arbitrary local solvers
Chenxin Ma, Jakub Konečný, Martin Jaggi, Virginia Smith, Michael I Jordan, Peter Richtárik, and Martin Takáč · 2017
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On randomized distributed coordinate descent with quantized updates
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Reddit comments dataset
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Randomized distributed mean estimation: Accuracy vs communication
Jakub Konečný and Peter Richtárik · 2016
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Federated learning: Collaborative machine learning without centralized training data
H. Brendan McMahan and Daniel Ramage · 2017
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Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and H. Brendan McMahan · 2017
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