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Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
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, G. Hinton, et al · 2009
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Personalized image aesthetics
J. Ren, X. Shen, Z. Lin, R. Mech, and D. J. Foran · 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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Leaf: A benchmark for federated settings
S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar · 2018
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Deep mutual learning
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu · 2018
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Federated learning with personalization layers
M. G. Arivazhagan, V. Aggarwal, A. K. Singh, and S. Choudhary · 2019
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Fedmd: Heterogenous federated learning via model distillation
D. Li and J. Wang · 2019
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On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2019
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Bayesian nonparametric federated learning of neural networks
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni · 2019
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Federated bayesian optimization via thompson sampling
Z. Dai, B. K. H. Low, and P. Jaillet · 2020
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Adaptive personalized federated learning
Y. Deng, M. M. Kamani, and M. Mahdavi · 2020
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Personalized federated learning: A meta-learning approach
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2020
Cited alongside, same era.
Federated learning of a mixture of global and local models
F. Hanzely and P. Richtárik · 2020
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
C. He, M. Annavaram, and S. Avestimehr · 2020
Cited alongside, same era.
The non-IID data quagmire of decentralized machine learning
K. Hsieh, A. Phanishayee, O. Mutlu, and P. Gibbons · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh · 2020
Cited alongside, same era.
Practical federated gradient boosting decision trees
Q. Li, Z. Wen, and B. He · 2020
Cited alongside, same era.
Federated learning with matched averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor · 2020
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Debiasing model updates for improving personalized federated training
D. A. E. Acar, Y. Zhao, R. Zhu, R. Matas, M. Mattina, P. Whatmough, and V. Saligrama · 2021
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Fedbe: Making bayesian model ensemble applicable to federated learning
H.-Y. Chen and W.-L. Chao · 2021
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Exploiting shared representations for personalized federated learning
L. Collins, H. Hassani, A. Mokhtari, and S. Shakkottai · 2021
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Personalized cross-silo federated learning on non-iid data
Y. Huang, L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y. Zhang · 2021
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
Cited alongside, same era.
Think locally, act globally: Federated learning with local and global representations
P. P. Liang, T. Liu, L. Ziyin, R. Salakhutdinov, and L.-P. Morency · 2020
Cited alongside, same era.
Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains
Q. Liu, Q. Dou, and P.-A. Heng · 2020
Cited alongside, same era.
Three approaches for personalization with applications to federated learning
Y. Mansour, M. Mohri, J. Ro, and A. T. Suresh · 2020
Cited alongside, same era.
Adaptive federated optimization
S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan · 2020
Cited alongside, same era.
Federated mutual learning
T. Shen, J. Zhang, X. Jia, F. Zhang, G. Huang, P. Zhou, K. Kuang, F. Wu, and C. Wu · 2020
Cited alongside, same era.
Later among the works it cites.
Ditto: Fair and robust federated learning through personalization
T. Li, S. Hu, A. Beirami, and V. Smith · 2021
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Fedbn: Federated learning on non-iid features via local batch normalization
X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou · 2021
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Federated multi-task learning under a mixture of distributions
O. Marfoq, G. Neglia, A. Bellet, L. Kameni, and R. Vidal · 2021
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Quped: Quantized personalization via distillation with applications to federated learning
K. Ozkara, N. Singh, D. Data, and S. Diggavi · 2021
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Personalized federated learning with first order model optimization
M. Zhang, K. Sapra, S. Fidler, S. Yeung, and J. M. Alvarez · 2021
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Federated learning on non-iid data silos: An experimental study
Q. Li, Y. Diao, Q. Chen, and B. He · 2022
Closest in time.
Fedme: Federated learning via model exchange
K. Matsuda, Y. Sasaki, C. Xiao, and M. Onizuka · 2022
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