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Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.
Protocols for secure computations
Andrew C. Yao · 1982
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How to generate and exchange secrets
Andrew Chi-Chih Yao · 1986
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Example-based learning for view-based human face detection
Kah-Kay Sung and Tomaso Poggio · 1998
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft · 2009
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Pedestrian detection: An evaluation of the state of the art
Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona · 2012
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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Cited alongside, same era.
T-cnn: Tubelets with convolutional neural networks for object detection from videos
Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, et al · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration
Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan · 2017
Federated learning for keyword spotting
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau · 2018
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Secure federated transfer learning
Yang Liu, Tianjian Chen, and Qiang Yang · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
Later among the works it cites.
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Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2017
Cited alongside, same era.
The EU General Data Protection Regulation (GDPR): A Practical Guide
Paul Voigt and Axel von dem Bussche · 2017
Cited alongside, same era.
LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
Later among the works it cites.
Object detection with deep learning: A review
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, and Xindong Wu · 2018
Later among the works it cites.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé M Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Closest in time.
Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays · 2019
Closest in time.
Secureboost: A lossless federated learning framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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