eprintid: 17894 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/94 datestamp: 2025-12-15 23:30:10 lastmod: 2025-12-15 23:30:11 status_changed: 2025-12-15 23:30:10 type: article metadata_visibility: show creators_name: Aggarwal, Meenakshi creators_name: Khullar, Vikas creators_name: Rani, Sunita creators_name: Prola, Thomas creators_name: Bhattacharjee, Shyama Barna creators_name: Shawon, Sarowar Morshed creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: thomas.prola@uneatlantico.es creators_id: creators_id: creators_id: title: Federated Learning on Internet of Things: Extensive and Systematic Review ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Internet of Things; federated learning; PRISMA; framework of FL; applications of FL; data privacy; communication 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. date: 2024-05 publication: Computers, Materials & Continua volume: 79 number: 2 pagerange: 1795-1834 id_number: doi:10.32604/cmc.2024.049846 refereed: TRUE issn: 1546-2226 official_url: http://doi.org/10.32604/cmc.2024.049846 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Aggarwal, Meenakshi; Khullar, Vikas; Rani, Sunita; Prola, Thomas; Bhattacharjee, Shyama Barna; Shawon, Sarowar Morshed y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, thomas.prola@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Federated Learning on Internet of Things: Extensive and Systematic Review. Computers, Materials & Continua, 79 (2). pp. 1795-1834. ISSN 1546-2226 document_url: http://repositorio.uneatlantico.es/id/eprint/17894/1/TSP_CMC_49846.pdf