Enhancing Distributed Machine Learning through Data Shuffling: Techniques, Challenges, and Implications
In distributed machine learning, data shuffling is a crucial data preprocessing technique that significantly impacts the efficiency and performance of model training. As distributed machine learning scales across multiple computing nodes, the ability to shuffle data effectively and efficiently has b...
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/04/itmconf_iwadi2024_03018.pdf |
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