Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Ahmed, Syed Thouheed; Mahesh, T. R.; Srividhya, E.; Vinoth Kumar, V.; Khan, Surbhi Bhatia; Albuali, Abdullah; Almusharraf, Ahlam

Abstract

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Citation

Ahmed, S. T., Mahesh, T. R., Srividhya, E., Vinoth Kumar, V., Khan, S. B., Albuali, A., & Almusharraf, A. (in press). Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework. BMC Medical Imaging, 24(1), 105. https://doi.org/10.1186/s12880-024-01279-4

• Journal Article Type: Article
• Acceptance Date: Apr 23, 2024
• Online Publication Date: May 10, 2024
• Deposit Date: May 21, 2024
• Publicly Available Date: May 21, 2024
• Journal: BMC Medical Imaging
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 1
• Pages: 105
• DOI: https://doi.org/10.1186/s12880-024-01279-4
• Keywords: Federated learning, Device categorization, Device labeling, Artificial intelligence of medical things, Healthcare systems