Guest Editorial Federated Learning for Privacy Preservation of Healthcare Data in Internet of Medical Things and Patient Monitoring

Thippa Reddy Gadekallu, Mamoun Alazab, Jude Hemanth, Weizheng Wang

    Research output: Contribution to journalEditorialpeer-review

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    Abstract

    The papers in this special section focus on federal learning applications for the Internet of Medical Things. Due to to the advancements in Internet of Medical Things (IoMT), wearable devices, remote monitoring of patients is possible like never before. Machine learning and deep learning techniques help the doctors immensely in remotely diagnosing the patients by learning the patterns from the data generated through these devices [1]. The main problem with traditional machine learning (ML)/deep learning (DL) models is that the data from the individual devices, sensors, wearables of patients have to be transferred to the central servers to train the data using the ML/DL models. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patients’ data to the central servers may create serious security and privacy issues.
    Original languageEnglish
    Pages (from-to)648-651
    Number of pages4
    JournalIEEE Journal of Biomedical and Health Informatics
    Volume27
    Issue number2
    DOIs
    Publication statusPublished - 1 Feb 2023

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