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Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2009
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agøura y Arcas · 2017
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Knowledge distillation by on-the-fly native ensemble
X. Lan, X. Zhu, and S. Gong · 2018
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Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, and Aurelien Bellet et. al · 2019
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
Cited alongside, same era.
SCAFFOLD: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
Cited alongside, same era.
Feature-level ensemble knowledge distillation for aggregating knowledge from multiple networks
SeongUk Park and Nojun Kwak · 2020
Cited alongside, same era.
Generalization in nli: Ways (not) to go beyond simple heuristics, 2021
Prajjwal Bhargava, Aleksandr Drozd, and Anna Rogers · 2021
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A Review on Ensemble Methods and their Applications to Optimization Problems
Carlos Camacho-Gómez, Sancho Salcedo-Sanz, and David Camacho · 2021
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Fedgems: Federated learning of larger server models via selective knowledge fusion
Sijie Cheng, Jingwen Wu, Yanghua Xiao, Yang Liu, and Yang Liu · 2021
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Personalized federated learning for heterogeneous clients with clustered knowledge transfer
Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu, and Gauri Joshi · 2021
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Periodic intra-ensemble knowledge distillation for reinforcement learning
Zhang-Wei Hong, Prabhat Nagarajan, and Guilherme Maeda · 2021
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Federated optimization for heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2020
Cited alongside, same era.
Hydra: Preserving ensemble diversity for model distillation
Linh Tran, Bastiaan S. Veeling, and Kevin Roth et. al · 2020
Cited alongside, same era.
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
Zeyuan Allen-Zhu and Yuanzhi Li · 2021
Cited alongside, same era.
Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data
Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto · 2021
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Model-contrastive federated learning
Qinbin Li, Bingsheng He, and Dawn Song · 2021
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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