TrimCaching: Parameter-Sharing AI Model Caching in Wireless Edge Networks

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement...

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Veröffentlicht in:	Proceedings of the International Conference on Distributed Computing Systems S. 36 - 46
Hauptverfasser:	Qu, Guanqiao, Lin, Zheng, Liu, Fangming, Chen, Xianhao, Huang, Kaibin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.07.2024
Schlagworte:	Approximation algorithms Computational modeling Edge AI Edge AI model caching edge computing edge intelligence Greedy algorithms model downloading Servers Simulation Wireless networks
ISSN:	2575-8411
Online-Zugang:	Volltext
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