Comparison of Federated Learning Strategies on ECG Classification
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Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Federated Learning has garnered considerable attention in recent years due to its capability to maintain data at its original location, thus preserving privacy and security while still yielding a high-quality generalized model. Like the privacy of any data, it is of paramount importance to protect the privacy of health data such as ECG recordings. Federated Learning, in this regard, can offer privacy and security in the classification of ECG data. In our study, we drew comparisons among the centralized model, Federated Learning models trained with both Independently and Identically Distributed (IID) data, and those trained with Non-IID data. For each model, we used Convolutional Neural Network (CNN) architecture, utilizing the MIT-BIH Arrhythmia Database as our dataset. While we achieve 98.39% accuracy with the centralized model, FedAvg with IID and Non-IID data provides 98.53%, and 87.70%, respectively. The results underline the effectiveness and challenges of various training data types in Federated Learning for ECG classification task. © 2023 IEEE.
Açıklama
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- -- 194153
Anahtar Kelimeler
Classification; ECG; Federated Learning, Classification (of information); Convolutional neural networks; Learning systems; Privacy-preserving techniques; Centralized models; Distributed data; ECG data; ECG recording; Federated learning; Generalized models; Health data; High quality; Learning strategy; Privacy and security; Electrocardiograms
