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Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module

Authors :
Maytham N. Meqdad
Abdullah Hasan Hussein
Saif O. Husain
Alyaa Mohammed Jawad
Source :
Indonesian Journal of Electrical Engineering and Computer Science. 30:936
Publication Year :
2023
Publisher :
Institute of Advanced Engineering and Science, 2023.

Abstract

Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.

Details

ISSN :
25024760 and 25024752
Volume :
30
Database :
OpenAIRE
Journal :
Indonesian Journal of Electrical Engineering and Computer Science
Accession number :
edsair.doi.dedup.....cc4b0d0a17c652f6f1ebc291a6b44130
Full Text :
https://doi.org/10.11591/ijeecs.v30.i2.pp936-943