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primary topic: Federated Learning on Internet of Things: Extensive and Systematic Review

Federated Learning on Internet of Things: Extensive and Systematic Review
type: AcademicArticle
type: Article
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 presen…
date: 2024-05
volume: 79
issue: 2
is part of: Computers, Materials & Continua
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Federated Learning on Internet of Things: Extensive and Systematic Review (Texto)
has document: Federated Learning on Internet of Things: Extensive and Systematic Review (Otro)
subject: Engineering

Federated Learning on Internet of Things: Extensive and Systematic Review (Texto)
type: Document
has file: TSP_CMC_49846.pdf
has part: TSP_CMC_49846.pdf

Computers, Materials & Continua
type: Collection
issn: 15462226

Nitin Goyal
type: Person
given name: Nitin
family name: Goyal

Sarowar Morshed Shawon
type: Person
given name: Sarowar Morshed
family name: Shawon

Meenakshi Aggarwal
type: Person
given name: Meenakshi
family name: Aggarwal

Sunita Rani
type: Person
given name: Sunita
family name: Rani

Thomas Prola
type: Person
given name: Thomas
family name: Prola

Shyama Barna Bhattacharjee
type: Person
given name: Shyama Barna
family name: Bhattacharjee

Vikas Khullar
type: Person
given name: Vikas
family name: Khullar

Federated Learning on Internet of Things: Extensive and Systematic Review (Otro)
type: Document
is version of: Federated Learning on Internet of Things: Extensive and Systematic Review (Texto)
is volatile version of: Federated Learning on Internet of Things: Extensive and Systematic Review (Texto)
is index codes version of: Federated Learning on Internet of Things: Extensive and Systematic Review (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17894 Federated Learning on Internet of Things: Extensive and Systematic Review
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primary topic: Federated Learning on Internet of Things: Extensive and Systematic Review

Engineering
type: Concept
preferred label: Ingeniería @es