Byzantine fault tolerance in distributed machine learning: a survey-Reference-Cited by-同舟云学术

Byzantine fault tolerance in distributed machine learning: a survey

Published:2024-09-12 Issue: Volume: Page:1-59
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Bouhata Djamila¹²,Moumen Hamouma¹²,Mazari Jocelyn Ahmed³⁴,Bounceur Ahcène⁵

Affiliation:

1. Computer Science Department, University of Batna, Batna, Algeria

2. Computer Science Department, Laboratory of Applications of Mathematics to Computer and Electronics, Batna, Algeria

3. ISIR, CNRS, Sorbonne Universite, Paris, France

4. Extrality, Paris, France

5. Information Systems Department, University of Sharjah, University City, Sharjah, United Arab Emirates

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2024.2391778

Reference191 articles.

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2. A survey on reliability in distributed systems

3. Alistarh, D., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic gradient descent. In: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 31. pp. 4613–4623). Curran Associates, Inc. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada. https://proceedings.neurips.cc/paper_files/paper/2018/file/a07c2f3b3b907aaf8436a26c6d77f0a2-Paper.pdf

4. On Fault Tolerant Training of Feedforward Neural Networks

5. Attiya, H., & Welch, J. (2004). Distributed computing: Fundamentals, simulations, and advanced topics (Vol. 19). John Wiley & Sons.