Towards Efficient Edge Learning with Limited Storage Resource: Bandit-Based Training Data Retrieval in AIoT

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

In an intelligent IoT environment, an edge server needs to retrieve data from end devices to train the deep neural network deployed at edge. In order to achieve higher training performance with limited resources, we propose a Bandit-based in-network training data retrieval scheme(Bandit-TDRetrieval). Specifically, we formulate data retrieval from end devices with a multi-armed bandit (MAB) model. A sequence of lever pulls of the arms, indicates the options to retrieve data from different end devices and follows a binomial distribution. To identify the relationship between this binomial distribution and the rewards through continuous data retrieval from the corresponding devices, Thompson sampling is used. In this way, an efficient data retrieval paradigm is proposed to maximize the rewards to retrieve meaningful training data for edge learning. Finally, the evaluations are performed on a NS-3-based simulation platform, which demonstrate the proposed paradigm effectively improve the training efficiency of the deep neural networks at edge.

Identifier

85184796955 (Scopus)

ISBN

[9783031477140]

Publication Title

Lecture Notes in Networks and Systems

External Full Text Location

https://doi.org/10.1007/978-3-031-47715-7_39

e-ISSN

23673389

ISSN

23673370

First Page

571

Last Page

588

Volume

824 LNNS

Fund Ref

Beijing University of Posts and Telecommunications

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