Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing

Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly...

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Vydané v:	IEEE transactions on computers Ročník 73; číslo 2; s. 466 - 480
Hlavní autori:	Xu, Zichuan, Wang, Lin, Liang, Weifa, Xia, Qiufen, Xu, Wenzheng, Zhou, Pan, Rana, Omer F.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Age Algorithms Approximation Approximation algorithms approximation and online algorithms Computational modeling Convergence data selection and aggregator placement Datasets Decision theory Delays Digital twins Distance learning Edge computing federated continual learning Federated learning Machine learning Mathematical analysis Mobile computing Mobile edge computing Model accuracy Optimization Optimization techniques Placement Prototypes Training
ISSN:	0018-9340, 1557-9956
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