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Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server.
Bandwidth optimal all-reduce algorithms for clusters of workstations
Pitch Patarasuk and Xin Yuan · 2009
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Parallelized stochastic gradient descent
Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J Smola · 2010
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Reducing the traffic bottleneck in cloud-based data management
Alexandros Fragkopoulos, Albena Mihovska, and Sofoklis Kyriazakos · 2013
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep learning with elastic averaging sgd
Sixin Zhang, Anna E Choromanska, and Yann LeCun · 2015
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Gossip dual averaging for decentralized optimization of pairwise functions
Igor Colin, Aurélien Bellet, Joseph Salmon, and Stéphan Clémençon · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Edge computing: Vision and challenges
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu · 2016
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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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Bringing HPC Techniques to Deep Learning
Andrew Gibiansky · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 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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Cocoa: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takáč, Michael I Jordan, and Martin Jaggi · 2017
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Decentralized collaborative learning of personalized models over networks
Paul Vanhaesebrouck, Aurélien Bellet, and Marc Tommasi · 2017
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https://www.opensignal.com/sites/opensignal-com/files/data/reports/global/data-2018-11/state_of_wifi_vs_mobile_opensignal_201811.pdf , 2018
The state of wifi vs mobile network experience as 5g arrives · 2018
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Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar · 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
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
Cited alongside, same era.
Horovod: fast and easy distributed deep learning in tensorflow
Alexander Sergeev and Mike Del Balso · 2018
Cited alongside, same era.
Deep learning-based classification of mesothelioma improves prediction of patient outcome
Pierre Courtiol, Charles Maussion, Matahi Moarii, Elodie Pronier, Samuel Pilcer, Meriem Sefta, Pierre Manceron, Sylvain Toldo, Mikhail Zaslavskiy, Nolwenn Le Stang, et al · 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, KA Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
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Federated learning for keyword spotting
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau · 2019
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Edge-assisted hierarchical federated learning with non-iid data
Lumin Liu, Jun Zhang, S. Song, and Khaled Letaief · 2019
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Sebastian U Stich · 2018
Cited alongside, same era.
Communication compression for decentralized training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, and Ji Liu · 2018
Cited alongside, same era.
D2: Decentralized training over decentralized data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu · 2018
Cited alongside, same era.
Atomo: Communication-efficient learning via atomic sparsification
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, and Stephen Wright · 2018
Cited alongside, same era.
Deeptype: On-device deep learning for input personalization service with minimal privacy concern
Mengwei Xu, Feng Qian, Qiaozhu Mei, Kang Huang, and Xuanzhe Liu · 2018
Cited alongside, same era.
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
Cited alongside, same era.
https://gdpr-info.eu/ , 2019
General data protection regulation (gdpr) · 2019
Cited alongside, same era.
Federated ai technology enabler
The FATE Authors · 2019
Cited alongside, same era.
Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
Later among the works it cites.
https://aws.amazon.com/ec2/pricing/on-demand/, 2020
Amazon ec2 on-demand pricing · 2020
Closest in time.
https://aws.amazon.com/about-aws/global-infrastructure/localzones/ , 2020
Aws local zones · 2020
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https://docs.microsoft.com/en-us/azure/networking/edge-zones-overview , 2020
Azure edge zone · 2020
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https://developer.android.com/training/connect-devices-wirelessly , 2020
Connect devices wirelessly in android · 2020
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Deep learning for Java
Eclipse. 2020 · 2020
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Hierarchical federated learning across heterogeneous cellular networks
Mehdi Salehi Heydar Abad, Emre Ozfatura, Deniz Gunduz, and Ozgur Ercetin · 2020
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Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Christopher Briggs, Zhong Fan, and Peter Andras · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
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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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Heterogeneity-aware federated learning
Chengxu Yang, QiPeng Wang, Mengwei Xu, Shangguang Wang, Kaigui Bian, and Xuanzhe Liu · 2020
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