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Distributed learning is central for large-scale training of deep-learning models.
The MNIST database of handwritten digits
LeCun, Y. (1998) · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Distributed dual averaging in networks
Agarwal, A., Wainwright, M. J., and Duchi, J. C. (2010) · 2010
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Recht, B., Re, C., Wright, S., and Niu, F. (2011) · 2011
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Poisoning attacks against support vector machines
Biggio, B., Nelson, B., and Laskov, P. (2012) · 2012
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Large scale distributed deep networks
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Senior, A., Tucker, P., Yang, K., Le, Q. V., et al. (2012) · 2012
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D. (2016) · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al. (2016) · 2016
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Adding gradient noise improves learning for very deep networks
Neelakantan, A., Vilnis, L., Le, Q. V., Sutskever, I., Kaiser, L., Kurach, K., and Martens, J. (2016) · 2016
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A uror: defending against poisoning attacks in collaborative deep learning systems
Shen, S., Tople, S., and Saxena, P. (2016) · 2016
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M. (2017) · 2017
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., Guerraoui, R., Stainer, J., et al. (2017) · 2017
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Learning discrete distributions from untrusted batches
Qiao, M. and Valiant, G. (2017) · 2017
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Shirish Keskar, N., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. (2017) · 2017
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Chen, B., Carvalho, W., Baracaldo, N., Ludwig, H., Edwards, B., Lee, T., Molloy, I., and Srivastava, B. (2018) · 2018
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The hidden vulnerability of distributed learning in Byzantium
El Mhamdi, E. M., Guerraoui, R., and Rouault, S. (2018) · 2018
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Mitigating sybils in federated learning poisoning
Fung, C., Yoon, C. J., and Beschastnikh, I. (2018) · 2018
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An alternative view: When does sgd escape local minima?
Kleinberg, R. D., Li, Y., and Yuan, Y. (2018) · 2018
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Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X. (2018) · 2018
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Spectral signatures in backdoor attacks
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Steinhardt, J., Koh, P. W. W., and Liang, P. S. (2017) · 2017
Cited alongside, same era.
Poseidon: An efficient communication architecture for distributed deep learning on GPU clusters
Zhang, H., Zheng, Z., Xu, S., Dai, W., Ho, Q., Liang, X., Hu, Z., Wei, J., Xie, P., and Xing, E. P. (2017) · 2017
Cited alongside, same era.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V. (2018) · 2018
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D. (2017a)
Cited in the paper.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y., Su, L., and Xu, J. (2017b)
Cited in the paper.
Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y. (2014a)
Cited in the paper.
Communication efficient distributed machine learning with the parameter server
Li, M., Andersen, D. G., Smola, A. J., and Yu, K. (2014b)
Cited in the paper.
Tran, B., Li, J., and Madry, A. (2018) · 2018
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Generalized Byzantine-tolerant SGD
Xie, C., Koyejo, O., and Gupta, I. (2018) · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Yin, D., Chen, Y., Ramchandran, K., and Bartlett, P. (2018) · 2018
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