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Group-Wise Verifiable Coded Computing Under Byzantine Attacks and Stragglers
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AI
DOI：10.1109/TIFS.2024.3377929.png)
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Distributed computing has emerged as a promising solution for accelerating machine learning training processes on large-scale datasets by leveraging the parallel processing capabilities of multiple workers. However, there remain two major issues that still need to be addressed: 1) Byzantine attacks from malicious workers; and 2) the effect of slow workers, commonly referred to as stragglers. In this paper, we address both issues concurrently by introducing Group-wise Verifiable Coded Computing (GVCC), a novel approach that combines coding techniques and group-wise verification to enhance robustness against Byzantine attacks and resilience to straggler effects in distributed computing. The key idea of GVCC is to verify a group of computation results from workers at a time, while providing resilience to stragglers through encoding tasks assigned to workers with Group-wise Verifiable Codes. We evaluate the performance of GVCC through experiments conducted on Amazon EC2 clouds and the results show that GVCC outperforms the existing methods in terms of overall processing time and verification time while maintaining the verification performance. This study highlights the potential of GVCC as an effective solution for overcoming the challenges of Byzantine attacks and stragglers in distributed computing for executing matrix multiplication.
Keyword:
Task analysis
Distributed computing
Encoding
Codes
Resilience
Machine learning
Decoding
Byzantine attacks
straggler effect
coded computing
期刊
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8
论文数:
5.3K
被引数:
2.3W
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