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We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication.
Communication-efficient algorithms for statistical optimization
Y. Zhang, M. Wainwright, and J. Duchi · 2012
Earlier work this paper cites.
Differentially private learning with kernels
P. Jain and A. Thakurta · 2013
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, B. McMahan, F. Yu, P. Richtárik, A. Suresh, and D. Bacon · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas · 2017
Cited alongside, same era.
Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
Later among the works it cites.
LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Later among the works it cites.
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