The DOI matches its Crossref record; 347 of the 352 references it cites were found in a registry or an index.
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Open accessAbstract
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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“For example, one study discovered that, in contrast to metropolitan settings with reliable internet access, remote healthcare institutions with inadequate internet access had communication delays that prolonged training periods by more than 50%.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [29]
In the citing paper
“Moreover, in some remote regions, internet access is so limited that models are only able to synchronize every few days, which significantly limits the overall learning process [29].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [29]
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“For instance, in applications such as autonomous vehicles [110], [111], [112] or mobile health monitoring systems [113], the capability of WFL to enable on-the-go learning and instantaneous model updating is indispensable.”
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Metadata
- 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
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 274
- Pages
- 110658
- ISSN
- 0950-7051 (ISSN-L)
- 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
- 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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