Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

Reading the bibliography…

Fetching the paper…

Built on

Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Distributed robust learning
Original
Feng, J., Xu, H., and Mannor, S · 2014
Earlier work this paper cites.
Mlitb: machine learning in the browser
Meeds, E., Hendriks, R., al Faraby, S., Bruntink, M., and Welling, M · 2015
Earlier work this paper cites.
Implementation of a practical distributed calculation system with browsers and javascript, and application to distributed deep learning
Original
Miura, K. and Harada, T · 2015
Earlier work this paper cites.

Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., Guerraoui, R., Stainer, J., et al · 2017
Cited alongside, same era.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y., Su, L., and Xu, J · 2017
Cited alongside, same era.
Byzantine stochastic gradient descent
Original
Alistarh, D., Allen-Zhu, Z., and Li, J · 2018
Cited alongside, same era.
Draco: Byzantine-resilient distributed training via redundant gradients
Chen, L., Wang, H., Charles, Z., and Papailiopoulos, D · 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
Original
Su, L. and Vaidya, N. H
Cited in the paper.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…

Reading the bibliography…

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance · Around

Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Distributed robust learning
Original
Feng, J., Xu, H., and Mannor, S · 2014
Earlier work this paper cites.
Mlitb: machine learning in the browser
Meeds, E., Hendriks, R., al Faraby, S., Bruntink, M., and Welling, M · 2015
Earlier work this paper cites.
Implementation of a practical distributed calculation system with browsers and javascript, and application to distributed deep learning
Original
Miura, K. and Harada, T · 2015
Earlier work this paper cites.

Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., Guerraoui, R., Stainer, J., et al · 2017
Cited alongside, same era.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Chen, Y., Su, L., and Xu, J · 2017
Cited alongside, same era.
Byzantine stochastic gradient descent
Original
Alistarh, D., Allen-Zhu, Z., and Li, J · 2018
Cited alongside, same era.
Draco: Byzantine-resilient distributed training via redundant gradients
Chen, L., Wang, H., Charles, Z., and Papailiopoulos, D · 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
Original
Su, L. and Vaidya, N. H
Cited in the paper.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…