Age-aware data selection and aggregator placement for timely federated continual learning in mobile edge computing

Xu, Zichuan, Wang, Lin, Liang, Weifa, Xia, Qiufen, Xu, Wenzheng, Zhou, Pan and Rana, Omer F. ORCID: https://orcid.org/0000-0003-3597-2646 2024. Age-aware data selection and aggregator placement for timely federated continual learning in mobile edge computing. IEEE Transactions on Computers 73 (2) , pp. 466-480. 10.1109/TC.2023.3333213

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Abstract

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 accurate machine learning model for new event predictions. The age of data , defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9340
Date of First Compliant Deposit:	22 November 2023
Date of Acceptance:	8 November 2023
Last Modified:	19 Feb 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/164223

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