作者
Tahani Aladwani, Christos Anagnostopoulos, Kostas Kolomvatsos, Ibrahim Alghamdi, Fani Deligianni
发表日期
2023/4/3
研讨会论文
2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)
页码范围
146-153
出版商
IEEE
简介
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 …
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T Aladwani, C Anagnostopoulos, K Kolomvatsos… - 2023 IEEE 39th International Conference on Data …, 2023