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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of computer science and technology Jg. 40; H. 3; S. 637 - 653
Hauptverfasser:	Peng, Xiao-Hui, Sun, Yi-Xuan, Zhang, Zheng-Hui, Wang, Yi-Fan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.05.2025 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!