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Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 1908
Earlier work this paper cites.
Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Federated learning: Collaborative machine learning without centralized training data, 2017
H Brendan McMahan and Daniel Ramage · 2017
Cited alongside, same era.
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 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.
Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
Jianyu Wang and Gauri Joshi · 2018
Later among the works it cites.
An investigation into on-device personalization of end-to-end automatic speech recognition models
Francoise Beaufays, Khe Chai Sim, and Petr Zadrazil · 2019
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, H Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Introducing tensorflow federated, 2019
Alex Ingerman and Krzys Ostrowski · 2019
Closest in time.
Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria Florina-Balcan, and Ameet Talwalkar · 2019
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečný, H Brendan McMahan, and Ameet Talwalkar
Cited in the paper.
Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar
Cited in the paper.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečný, H Brendan McMahan, Daniel Ramage, and Peter Richtárik
Cited in the paper.
Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon
Cited in the paper.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Closest in time.
Lookahead optimizer: k steps forward, 1 step back
Michael R Zhang, James Lucas, Geoffrey Hinton, and Jimmy Ba · 2019
Closest in time.