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In federated learning, a central server coordinates the training of a single model on a massively distributed network of devices.
Gradient-based learning applied to document recognition
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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The matrix cookbook, vol. 7
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Distributed training strategies for the structured perceptron
Ryan McDonald, Keith Hall, and Gideon Mann · 2010
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Large scale distributed deep networks
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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 · 2012
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A public domain dataset for human activity recognition using smartphones
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Streaming variational bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, and Michael I Jordan · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Communication-efficient distributed dual coordinate ascent
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I Jordan · 2014
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Parallel training of deep neural networks with natural gradient and parameter averaging
Daniel Povey, Xiaohui Zhang, and Sanjeev Khudanpur · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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The unreasonable effectiveness of recurrent neural networks
Andrej Karpathy · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 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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Knowledge transfer in deep block-modular neural networks
The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Partitioned variational inference: A unified framework encompassing federated and continual learning
Thang D Bui, Cuong V Nguyen, Siddharth Swaroop, and Richard E Turner · 2018
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Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Alexander V Terekhov, Guglielmo Montone, and J Kevin O’Regan · 2015
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Word representations via gaussian embedding
Luke Vilnis and Andrew McCallum · 2015
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Deep learning with elastic averaging sgd
Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečny, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Emnist: an extension of mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Joshua V Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, and Rif A Saurous · 2017
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Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
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A performance evaluation of federated learning algorithms
Adrian Nilsson, Simon Smith, Gregor Ulm, Emil Gustavsson, and Mats Jirstrand · 2018
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On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Cocoa: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Ma Chenxin, Martin Takáč, Michael I Jordan, and Martin Jaggi · 2018
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Memory replay gans: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, and Bogdan Raducanu · 2018
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Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Federated collaborative filtering for privacy-preserving personalized recommendation system
Muhammad Ammad-ud din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan · 2019
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Transfer learning by adaptive merging of multiple models
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Oracle: Order robust adaptive continual learning
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Bayesian nonparametric federated learning of neural networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni · 2019
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Model fusion via optimal transport
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Expectation propagation as a way of life: A framework for bayesian inference on partitioned data
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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