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Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Federated learning: Strategies for improving communication efficiency
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon · 2016
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Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 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
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S Talwalkar · 2017
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Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data
E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
Cited alongside, same era.
Robust federated learning in a heterogeneous environment
A. Ghosh, J. Hong, D. Yin, and K. Ramchandran · 2019
Cited alongside, same era.
Learning private neural language modeling with attentive aggregation
S. Ji, S. Pan, G. Long, X. Li, J. Jiang, and Z. Huang · 2019
Cited alongside, same era.
Incentive design for efficient federated learning in mobile networks: A contract theory approach
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, and Dong In Kim · 2019
Cited alongside, same era.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2019
Cited alongside, same era.
Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Adaptive personalized federated learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Cited alongside, same era.
Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
Cited alongside, same era.
Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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A fairness-aware incentive scheme for federated learning
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang · 2020
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Fedmix: Approximation of mixup under mean augmented federated learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang · 2021
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