Query-driven Edge Node Selection in Distributed Learning Environments

Aladwani, T., Anagnostopoulos, C. , Kolomvatsos, K., Alghamdi, I. and Deligianni, F. (2023) Query-driven Edge Node Selection in Distributed Learning Environments. In: Data-driven Smart Cities (DASC 2023)/ 39th IEEE International Conference on Data Engineering (ICDE 2023), Anaheim, CA, United States, 3-7 April 2023, pp. 146-153. ISBN 9798350322446 (doi: 10.1109/ICDEW58674.2023.00029)

Abstract

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Anagnostopoulos, Dr Christos and Deligianni, Dr Fani and Aladwani, Ms Tahani
• Authors: Aladwani, T., Anagnostopoulos, C., Kolomvatsos, K., Alghamdi, I., and Deligianni, F.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2473-3490
• ISBN: 9798350322446
• Copyright Holders: Copyright © 2023, IEEE
• First Published: 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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