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| Title | Federated Learning On Internet Of Things: Extensive And Systematic Review |
|---|---|
| ID_Doc | 26365 |
| Authors | Aggarwal M.; Khullar V.; Rani S.; André Prola T.; Bhattacharjee S.B.; Shawon S.M.; Goyal N. |
| Year | 2024 |
| Published | Computers, Materials and Continua, 79, 2 |
| DOI | http://dx.doi.org/10.32604/cmc.2024.049846 |
| Abstract | The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles. A search was performed in the IEEE, Elsevier, Arxiv, ACM, and WOS databases and 92 articles were finally examined. Inclusion measures were published in English and with the keywords “FL” and “IoT”. The methodology begins with an overview of recent advances in FL and the IoT, followed by a discussion of how these two technologies can be integrated. To be more specific, we examine and evaluate the capabilities of FL by talking about communication protocols, frameworks and architecture. We then present a comprehensive analysis of the use of FL in a number of key IoT applications, including smart healthcare, smart transportation, smart cities, smart industry, smart finance, and smart agriculture. The key findings from this analysis of FL IoT services and applications are also presented. Finally, we performed a comparative analysis with FL IID (independent and identical data) and non-ID, traditional centralized deep learning (DL) approaches. We concluded that FL has better performance, especially in terms of privacy protection and resource utilization. FL is excellent for preserving privacy because model training takes place on individual devices or edge nodes, eliminating the need for centralized data aggregation, which poses significant privacy risks. To facilitate development in this rapidly evolving field, the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT. © 2024 Tech Science Press. All rights reserved. |
| Author Keywords | applications of FL; communication; data privacy; federated learning; framework of FL; Internet of Things; PRISMA |
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