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Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning.
On the bounds of the range of order statistics
Hawkins, D. M · 1971
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Bounds on expectations of linear systematic statistics based on dependent samples
Arnold, B. C., Groeneveld, R. A., et al · 1979
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Impossibility of distributed consensus with one faulty process
Fischer, M. J., Lynch, N. A., and Paterson, M. S · 1982
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The byzantine generals problem
Lamport, L., Shostak, R. E., and Pease, M. C · 1982
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Distributed algorithms
Lynch, N. A · 1996
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Basic concepts and taxonomy of dependable and secure computing
Avizienis, A., Laprie, J.-C., Randell, B., and Landwehr, C · 2004
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Distributed systems: principles and paradigms
Tanenbaum, A. S. and Van Steen, M · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Feng, J., Xu, H., and Mannor, S · 2014
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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
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Strategies and principles of distributed machine learning on big data
Xing, E. P., Ho, Q., Xie, P., and Wei, D · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., Guerraoui, R., Stainer, J., et al · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2018
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S · 2018
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Draco: Byzantine-resilient distributed training via redundant gradients
Chen, L., Wang, H., Charles, Z., and Papailiopoulos, D · 2018
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The hidden vulnerability of distributed learning in byzantium
Guerraoui, R., Rouault, S., et al · 2018
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Chen, Y., Su, L., and Xu, J · 2017
Cited alongside, same era.
Byzantine stochastic gradient descent
Alistarh, D., Allen-Zhu, Z., and Li, J · 2018
Cited alongside, same era.
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
Cited in the paper.
Communication efficient distributed machine learning with the parameter server
Li, M., Andersen, D. G., Smola, A. J., and Yu, K
Cited in the paper.
Fault-tolerant multi-agent optimization: Optimal iterative distributed algorithms
Su, L. and Vaidya, N. H
Cited in the paper.
Defending non-bayesian learning against adversarial attacks
Su, L. and Vaidya, N. H
Cited in the paper.
Yin, D., Chen, Y., Ramchandran, K., and Bartlett, P · 2018
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