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The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2001
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2001
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
http://www.speedtest.net/reports/united-states/ , 2016
speedtest.net. speedtest market report · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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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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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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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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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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https:// support.google.com/messages/answer/9327902 , Aug., 2019
support.google. your chats stay private while messages improves suggestions · 2019
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Geometric lower bounds for distributed parameter estimation under communication constraints
Yanjun Han, Ayfer Özgür, and Tsachy Weissman · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Robust and communication-efficient collaborative learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, and Ramtin Pedarsani · 2019
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An investigation into on-device personalization of end-to-end automatic speech recognition models
Khe Chai Sim, Petr Zadrazil, and Françoise Beaufays · 2019
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Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 2019
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Apple. designing for privacy (video and slide deck)
Apple WWDC · 2019
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Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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