The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT
Open accessAbstract
The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.
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In the citing paper
“Singh et al. [47] introduced a Hierarchical FL (HFL) model based on Dew-Cloud technology, which offers an enhanced level of data confidentiality for Internet of Medical Things (IoMT) applications. In this study, for detecting intrusion in incoming traffic a hierarchical long-term memory (HLSTM) model has also been deployed at distributed Dew servers. The authors claim that the proposed model can”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [47]
In the citing paper
“Hence few works concentrated on the development of a hierarchical FL approach that permits aggregation at varying levels, facilitating collaboration among multiple parties [46,47,48].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [47]
In the citing paper
“These approaches are effective and relevant for enhancing privacy preservation in FL systems as they provide additional layers of control over data sharing among clients, edge and cloud servers.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [47]
In the citing paper
“By leveraging hierarchical structures to organize clients and servers, privacy-preserving collaboration is facilitated [46,47,48].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [47]
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Cites
Industrial Internet of Things: Challenges, Opportunities, and Directions
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2852491 · Cited by 2,380
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A. N., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 1,051
Medical Internet of Things and Big Data in Healthcare
Dimitrov, D. V. (2016) · Healthcare Informatics Research · doi:10.4258/hir.2016.22.3.156 · Cited by 975
Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures
Bütün, İ., Osterberg, P., & Song, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2953364 · Cited by 792
Moustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 747
BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network
Gope, P., & Hwang, T. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2502401 · Cited by 723
DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
Li, B., Wu, Y., Song, J., Lu, R., Li, T., & Zhao, L. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3023430 · Cited by 648
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Li, J., Luong, M.-T., & Jurafsky, D. (2015) · doi:10.3115/v1/p15-1107 · Cited by 511
Security and Privacy in Smart Health: Efficient Policy-Hiding Attribute-Based Access Control
Zhang, Y., Zheng, D., & Deng, R. H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2825289 · Cited by 488
Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security
Ghubaish, A., Salman, T., Zolanvari, M., Ünal, D., Al-Ali, A. K., & Jain, R. K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3045653 · Cited by 480
Research works printed but not linked: 1
- Schneble, 2019, Attack detection using federated learning in medical cyber-physical systems, 29, 1
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Sorted by citations
Items 101–104 of 104
FedMedIDS: A non-IID-resilient federated DCNN framework for securing IoMT and IoHT devices
Gencer, B., & Doğan, G. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102093
Ravikumar, G., Giriprasad, S., & Gokul, S. (2026) · Measurement · doi:10.1016/j.measurement.2026.123208
Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare
Satav, S. D., Sadhana, R., Vaitheeswari, A., Chokkalingam, A., Sultanuddin, S. J., & Indra, G. (2026) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc70182.2026.11687605
Hierarchical federated learning for the Internet of Things: A comprehensive survey
Mostafavi, S. (2026) · Array · doi:10.1016/j.array.2026.101387
Metadata
- Authors
- Singh, P., Gaba, G. S., Kaur, A., Hedabou, M., & Gurtov, A.
- Affiliations
- Lovely Professional University (IN)
- Linköping University (SE)
- Université Mohammed VI Polytechnique (MA)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2023-02
- Volume
- 27
- Issue
- 2
- Pages
- 722-731
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4285030518
- Cited by
- 103
- References
- 51
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 30 · 2023: 15
- FWCI
- 6.75
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- Funders
- CENIIT (17.01)
- Excellence Center at Linköping-Lund in IT
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- hierarchical federated learning, intrusion detection, Internet of Medical Things, medical device security, data privacy, long short-term memory, cloud computing, resource-constrained devices, medical data protection
- MeSH terms
- Cloud Computing, Internet of Things, COVID-19, Algorithms, Humans, Internet
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