IIOT: An Infusion of Embedded Systems, TinyML, and Federated Learning in Industrial IoT

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ID: 312757
2025
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Abstract
With the revolution of Industrial 5.0 the system was modified to smart manufacturing. This Industrial 5.0 is emerging with different technologies such as IoT which provide real-time monitoring, analysis, and data fetching. For the novelty in II0T Application, this article investigates the combination of embedded systems, Tiny Machine Learning (TinyML), and Federated Learning (FL). Data privacy is ensured by Federated Learning (FL), and local data processing becomes efficient through Tiny Machine Learning (TinyML). This infusion promises to decrease latency, increase productivity, and improve data security. As previously unsolvable issues or problems are being addressed with renewed enthusiasm, new paradigms for development and research are needed. The goal of this article is to provide a platform and overcome the knowledge gaps for future revolutionary research projects that will leverage the growing trends of embedded devices influenced by compressed artificial intelligence (AI) models [18]. Moreover, will discuss about the TinyML, federated learning (FL) that permits the models to be trained locally on edge devices by utilizing their data as well as reducing the requirement for centralized data accumulation that may be even impossible in fewer Internet of Things (IoT) situations. It charts the development of embedded devices and wireless communication technologies, demonstrating the advent of Internet of Things applications across a spectrum of industries. In addition, the paper conducts a thorough cutting-edge technology to find recent works that use TinyML models to readily available embedded devices, and talks about recent research trends.
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Authors Muhammad Yousif
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
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URL
https://jcbi.org/index.php/Main/article/view/911
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