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Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.
Untraceable electronic mail, return addresses, and digital pseudonyms
David L. Chaum · 1981
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Long short-term memory
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Simple demographics often identify people uniquely
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Learning multiple layers of features from tiny images
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Parallelized stochastic gradient descent
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Large scale distributed deep networks
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Communication-efficient algorithms for statistical optimization
Yuchen Zhang, Martin J Wainwright, and John C Duchi · 2012
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Secure multiparty aggregation with differential privacy: A comparative study
Slawomir Goryczka, Li Xiong, and Vaidy Sunderam · 2013
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Communication efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2013
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Consumer data privacy in a networked world: A framework for protecting privacy and promoting innovation in the global digital economy
White House Report · 2013
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Trading computation for communication: Distributed stochastic dual coordinate ascent
Tianbao Yang · 2013
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Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Yuchen Zhang, John Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N. Dauphin, Razvan Pascanu, Çaglar Gülçehre, KyungHyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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Privacy aware learning
John Duchi, Michael I. Jordan, and Martin J. Wainwright · 2014
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The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
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Fast distributed coordinate descent for non-strongly convex losses
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Character-aware neural language models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M. Rush · 2015
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Adding vs. averaging in distributed primal-dual optimization
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I Jordan, Peter Richtárik, and Martin Takáč · 2015
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Parallel training of deep neural networks with natural gradient and parameter averaging
Daniel Povey, Xiaohui Zhang, and Sanjeev Khudanpur · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Deep learning with elastic averaging sgd
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Olivier Fercoq, Zheng Qu, Peter Richtárik, and Martin Takác · 2014
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Benjamin Graham · 2014
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Distributed stochastic optimization and learning
Ohad Shamir and Nathan Srebro · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Asynchronous distributed admm for consensus optimization
Ruiliang Zhang and James Kwok · 2014
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Technology device ownership: 2015
Monica Anderson · 2015
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Communication complexity of distributed convex learning and optimization
Yossi Arjevani and Ohad Shamir · 2015
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Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015
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Communication-efficient distributed optimization of self-concordant empirical loss
Yuchen Zhang and Lin Xiao · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Practical secure aggregation for federated learning on user-held data
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Deep learning
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Federated learning: Strategies for improving communication efficiency
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Smartphone ownership and internet usage continues to climb in emerging economies
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Tensorflow convolutional neural networks tutorial, 2016
TensorFlow team · 2016
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