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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.<\/jats:p>","DOI":"10.1186\/s12880-024-01279-4","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T14:01:25Z","timestamp":1715349685000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework"],"prefix":"10.1186","volume":"24","author":[{"given":"Syed Thouheed","family":"Ahmed","sequence":"first","affiliation":[]},{"given":"T. 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