DSparse: A Distributed Training Method for Edge Clusters Based on Sparse Update

Edge machine learning creates a new computational paradigm by enabling the deployment of intelligent applications at the network edge. It enhances application efficiency and responsiveness by performing inference and training tasks closer to data sources. However, it encounters several challenges in...

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Vydáno v:	Journal of computer science and technology Ročník 40; číslo 3; s. 637 - 653
Hlavní autoři:	Peng, Xiao-Hui, Sun, Yi-Xuan, Zhang, Zheng-Hui, Wang, Yi-Fan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.05.2025 Springer Nature B.V
Témata:	Accuracy Artificial Intelligence Bandwidths Clusters Communication Computer centers Computer Science Consumption Costs Data Structures and Information Theory Datasets Edge computing Efficiency Inference Information Systems Applications (incl.Internet) Internet of Things Lifelong learning Machine learning Memory devices Neural networks Parameters Privacy Regular Paper Software Engineering Theory of Computation distributed training edge machine learning sparse update edge computing edge cluster
ISSN:	1000-9000, 1860-4749
On-line přístup:	Získat plný text
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