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Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Optimal kernel choice for large-scale two-sample tests
A. Gretton, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu, and B. K. Sriperumbudur · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio · 2013
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Transfer feature learning with joint distribution adaptation
M. Long, J. Wang, G. Ding, J. Sun, and S. Y. Philip · 2013
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Federated optimization: Distributed optimization beyond the datacenter
J. Konečnỳ, B. McMahan, and D. Ramage · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Cited alongside, same era.
Federated multi-task learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar · 2017
Cited alongside, same era.
Distributed mean estimation with limited communication
A. T. Suresh, F. X. Yu, S. Kumar, and H. B. McMahan · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
F. Zenke, B. Poole, and S. Ganguli · 2017
Expanding the reach of federated learning by reducing client resource requirements
S. Caldas, J. Konečny, H. B. McMahan, and A. Talwalkar · 2018
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Leaf: A benchmark for federated settings
S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
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Federated meta-learning for recommendation
F. Chen, Z. Dong, Z. Li, and X. He · 2018
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E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim · 2018
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Two-stream federated learning: Reduce the communication costs
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Cited alongside, same era.
Deep unsupervised convolutional domain adaptation
J. Zhuo, S. Wang, W. Zhang, and Q. Huang · 2017
Cited alongside, same era.
X. Yao, C. Huang, and L. Sun · 2018
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Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
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Towards faster and better federated learning: A feature fusion approach
X. Yao, T. Huang, C. Wu, R. Zhang, and L. Sun · 2019
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