Fetching the paper…
Reading the bibliography…
As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft · 2009
Earlier work this paper cites.
A near-optimal algorithm for differentially-private principal components
Kamalika Chaudhuri, Anand D Sarwate, and Kaushik Sinha · 2013
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Revisiting distributed synchronous sgd
Jianmin Chen, Rajat Monga, Samy Bengio, and Rafal Jozefowicz · 2016
Earlier work this paper cites.
Staleness-aware async-sgd for distributed deep learning
Wei Zhang, Suyog Gupta, Xiangru Lian, and Ji Liu · 2016
Earlier work this paper cites.
Model accuracy and runtime tradeoff in distributed deep learning: A systematic study
Wei Zhang, Suyog Gupta, and Fei Wang · 2016
Earlier work this paper cites.
Chainermn: scalable distributed deep learning framework
Takuya Akiba, Keisuke Fukuda, and Shuji Suzuki · 2017
Earlier work this paper cites.
Fast and differentially private algorithms for decentralized collaborative machine learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi · 2017
Earlier work this paper cites.
Personalized and private peer-to-peer machine learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi · 2017
Cited alongside, same era.
Accurate, large minibatch SGD: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 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.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
Later among the works it cites.
Towards decentralized deep learning with differential privacy
Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Syed Zawad, Feng Yan, Shiyu Li, Hai Li, and Yiran Chen · 2019
Later among the works it cites.
Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ilya Mironov · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Imagenet training in 24 minutes
Yang You, Zhao Zhang, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Leasgd: an efficient and privacy-preserving decentralized algorithm for distributed learning
Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Feng Yan, Shiyu Li, Hai Li, and Yiran Chen · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Asynchronous decentralized parallel stochastic gradient descent
X Lian, W Zhang, C Zhang, and J Liu · 2018
Cited alongside, same era.
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Biscotti: A ledger for private and secure peer-to-peer machine learning
Muhammad Shayan, Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2018
Cited alongside, same era.
Anusha Lalitha, Xinghan Wang, Osman Kilinc, Yongxi Lu, Tara Javidi, and Farinaz Koushanfar · 2019
Later among the works it cites.
Asynchronous federated learning with differential privacy for edge intelligence
Yanan Li, Shusen Yang, Xuebin Ren, and Cong Zhao · 2019
Later among the works it cites.
Differentially private asynchronous federated learning for mobile edge computing in urban informatics
Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang · 2019
Later among the works it cites.
Braintorrent: A peer-to-peer environment for decentralized federated learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
Later among the works it cites.
Efficient privacy-preserving nonconvex optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, and Quanquan Gu · 2019
Later among the works it cites.
Distributed deep learning strategies for automatic speech recognition
Wei Zhang, Xiaodong Cui, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung, and Michael Picheny · 2019
Later among the works it cites.
A highly efficient distributed deep learning system for automatic speech recognition
Wei Zhang, Xiaodong Cui, Ulrich Finkler, George Saon, Abdullah Kayi, Alper Buyuktosunoglu, Brian Kingsbury, David Kung, and Michael Picheny · 2019
Later among the works it cites.
Improving efficiency in large-scale decentralized distributed training
Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David Kung, and Michael Picheny · 2020
Closest in time.