Fetching the paper…
Reading the bibliography…
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE
1998
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” in Technical Report, University of Toronto
2009
Earlier work this paper cites.
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010
2010
Earlier work this paper cites.
J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research
2011
Earlier work this paper cites.
A. Rakhlin, O. Shamir, K. Sridharan, et al
2012
Earlier work this paper cites.
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, Q. V. Le, et al
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems
2012
Earlier work this paper cites.
S. Ghadimi and G. Lan, “Stochastic first-and zeroth-order methods for nonconvex stochastic programming,” SIAM Journal on Optimization
2013
Earlier work this paper cites.
B. Graham, “Fractional max-pooling,” arXiv preprint arXiv:1412.6071
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
D. Kinga and J. B. Adam, “A method for stochastic optimization,” in International Conference on Learning Representations (ICLR)
2015
Cited alongside, same era.
H. B. McMahan and D. Ramage, “Federated learning: Collaborative machine learning without centralized training data,” in Google
2017
Later among the works it cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
2017
Later among the works it cites.
2017
Later among the works it cites.
T. Tieleman and G. Hinton, “Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning,” tech. rep., Technical Report. Available online: https://zh. coursera. org/learn/neuralnetworks/lecture/YQHki/rmsprop-divide-the-gradient-by-a-running-average-of-its-recent-magnitude (accessed on 21 April 2017)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2017
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Int. Conf. on Artificial Intelligence and Statistics
2017
Cited alongside, same era.
W. Shakespeare, “The complete works of william shakespeare,” in https://www.gutenberg.org/ebooks/100
Cited in the paper.
2017
Later among the works it cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems
2017
Later among the works it cites.
2018
Closest in time.