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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Vydáno v:IEEE transactions on computers Ročník 73; číslo 2; s. 466 - 480
Hlavní autoři: Xu, Zichuan, Wang, Lin, Liang, Weifa, Xia, Qiufen, Xu, Wenzheng, Zhou, Pan, Rana, Omer F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2023.3333213