Cited by 228Privacy-Preserving Technologies in DataInternet of Things and AIIoT and Edge/Fog Computing

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

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Rani, Sita, Kataria, Aman, Kumar, Sachin, Tiwari, Prayag·2023·Knowledge-Based Systems

DOI 10.1016/j.knosys.2023.110658

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Abstract

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks.

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Authors: Rani, S., Kataria, A., Kumar, S., & Tiwari, P.
Affiliations: Guru Nanak Dev University (IN)
Central Scientific Instruments Organisation (IN)
South Ural State University (RU)
Halmstad University (SE)
Corresponding author: Tiwari, Prayag
ORCID: Rani, Sita
Kataria, Aman
Kumar, Sachin
Tiwari, Prayag
Venue: Knowledge-Based Systems
Publisher: Elsevier BV
Published: 2023-08
Volume: 274
Pages: 110658
ISSN: 0950-7051 (ISSN-L)
DOI: 10.1016/j.knosys.2023.110658
OpenAlex: W4377294193
Semantic Scholar: f1ac75d96c85121f4e0e52ff8c001ac7f3173f8b
Cited by: 228
References: 352
Citations by year: 2026: 54 · 2025: 94 · 2024: 65 · 2023: 15
FWCI: 20.18
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Knowledge-Based Systems (publishedVersion, cc-by)
Funders: Russian Science Foundation (23-21-10009)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords: federated learning, Internet of Medical Things, smart healthcare, data privacy, medical device security, secure machine learning, distributed training, healthcare data security, privacy-preserving healthcare

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

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