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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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High security and privacy protection model for STI/HIV risk prediction
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“9,10 FL provides several signi fi cant bene fi ts when implemented in healthcare settings, effectively addressing many of the challenges associated with the sharing of sensitive medical data, such as data relating to clients ’ STI/HIV status, while enabling advanced applications of deep learning and…”
Farooqi, S. A., Rahman, A. B. A., & Saad, A. B. (2025) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom64595.2025.10857505 · Cited by 6
Technology and Gender Violence: Victimization Model, Consequences and Measures
Rani, S., Kaur, J., & Bhambri, P. (2023) · Signals and communication technology · doi:10.1007/978-3-031-45237-6_1 · Cited by 7
Self-citationExploring Uncertainty in Medical Federated Learning: A Survey
Zeng, X., Ahmed, A., & Tunio, M. H. (2025) · Electronics · doi:10.3390/electronics14204072 · Cited by 5
A Blockchain Identity Privacy Management Framework for a Healthcare Application
Sakka, S., Liagkou, V., & Stylios, C. D. (2024) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain62396.2024.00088 · Cited by 5
“These regulations present numerous challenges for advanced data processing techniques, such as Machine Learning (ML) and Deep Learning (DL), which require a substantial amount of training data [30], [31].”
BackgroundRobust decentralized federated learning for heterogeneous and non-ideal networks
Li, B., Gao, W., Xie, J., Li, H., & Gong, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111362 · Cited by 5
MediGuard: A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications
Sutradhar, S., Bose, R., Majumder, S., Khan, A. A., Roy, S., Ullah, F., & Prashar, D. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.061965 · Cited by 5
Recent Trends of Federated Learning for Smart Healthcare Systems
Handa, T., Singhal, I., Chakraborty, P., & Kaur, G. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1082-3.ch005 · Cited by 5
Advancements in Energy Harvesting
Kataria, A., & Rani, S. (2024) · Advances in civil and industrial engineering book series · doi:10.4018/979-8-3693-2003-7.ch009 · Cited by 5
Self-citationPrivacy-Preserving Federated Learning Framework for IoMT Smart Healthcare Applications
Manzoor, S. I., Jain, S., & Singh, Y. P. (2025) · International Conference on Electronics, AI and Computing (EAIC) · doi:10.1109/eaic66483.2025.11101398 · Cited by 4
“These gadgets are attached to a person’s body and gather medical information about their health [25].”
Towards advanced AI-based solutions for securing IoMT in smart health information systems
Liu, X. (2026) · Scientific Reports · doi:10.1038/s41598-025-31850-0 · Cited by 4
Xin, H., Ajibade, S.-S. M., Alhassan, G. N., & Yilmaz, Y. (2026) · Digital Health · doi:10.1177/20552076251395701 · Cited by 5
Security challenges and solutions in Internet of Medical Things (IoMT) communication: A review
Mohammed, B. A., Al-Shareeda, . A., Hamzah, A. E., Alhasnawi, B. N., Homod, R. Z., Alkhabra, Y. A., Al-Mekhlafi, Z. G., alrashdi, R., Alshammari, G., & Alreshidi, I. D. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00658-x · Cited by 4
“…of decoupling from training data and fail to capture dynamic relationships while modern AI systems can contribute a dynamic solu-tion to IoMT security by learning from historical data incidents and leverage this information for threat evolution detection (Mohammed et al. 2023; Rani et al. 2023).”
Digital technology's impact on knowledge management in the healthcare industry in the Middle East
Alhashmi, S. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30168-1.00021-9 · Cited by 4
Hu, H., Du, W., Li, Y., & Wang, Y. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107849 · Cited by 4
GAPLG: Graph Augmented With Pseudolabels Generation for Blockchain Anomaly Transaction Detection
Huang, J., Bu, K., Han, H., Gong, B., Xiong, A., Wang, W., & Wu, Q. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3555658 · Cited by 4
“Aside from money laundering, various other illicit activities are concealed within Bitcoin transactions, such as fraud [3], [4], phishing [5], and Ponzi schemes [6].”
Blockchain-Based Security Sustainable Framework for IoMT Applications and Industry 5.0
Baniya, P., Agrawal, A., Nand, P., Bhushan, B., & Bhattacharya, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-69336-6_17 · Cited by 4
Zhou, A., & Piramuthu, S. (2024) · International Conference on Mobile and Secure Services (MobiSecServ) · doi:10.1109/mobisecserv63327.2024.10760186 · Cited by 4
“1) Big Data and Predictive Analytics: Data analytics can predict patient behavior, health outcomes, or risks, but such predictions can be misused [56].”
AAMB: a cross-domain identity authentication scheme based on multilayered blockchains in IoMT
Zhang, Z., Li, H., Li, D., & Li, K. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07309-4 · Cited by 4
Federated Learning for Early Cardiac Anomaly Prediction in Cross-Silo IoMT Environments
Georgiades, M., Christodoulou, L., Chari, A., Wang, K., Ho, K.-H., Hou, Y., & Chai, W. K. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00087 · Cited by 4
“Finally, systems-level and implementation perspectives are provided by Gupta et al. [9], Rani et al. [8], and Yoo et al. [47].”
Machine Learning Based Remote Health Care Data Monitoring and Prediction System Using IoMT
Ramalingam, S., Dharani, S., Priya, R., Devi, E. P., & Pavithra, S. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10716978 · Cited by 4
Hybrid Ensemble Lightweight Cryptosystem for Internet of Medical Things Security
Swetha, M., & Rao, G. A. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.625 · Cited by 4
“Each round is divided into two stages: the first stage is the substitution step, and the second stage is the permutation step [37].”
Legal implications of federated learning integration in digital healthcare systems
Imoize, A. L., Obaidat, M. S., & Song, H. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00014-x · Cited by 3
Khan, Z. A., Xia, Y., Jiang, W., & Anwar, M. S. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3599558 · Cited by 3
“To make emotion detection viable for large-scale healthcare IoMT applications, solutions must address these challenges while maintaining high accuracy and low latency [7].”
Integration of metaheuristic and machine learning in Cloud-Fog-Edge IoMT applications: a survey
Lalama, Z., Goudjil, L., Cherbal, S., & Louail, L. (2025) · Cluster Computing · doi:10.1007/s10586-025-05442-6 · Cited by 3
“Authors of [37], presented a comprehensive survey of the security of IoMT applications, it focuses on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets.”
Federated deep learning with clustering-based aggregation for medical diagnosis
Al-Refai, M., Alkhaza’leh, S., & Alzu’bi, A. (2025) · Cluster Computing · doi:10.1007/s10586-025-05623-3 · Cited by 3
“By training models on diverse data while preserving privacy, federated learning enables the development of personalized and context-aware solutions for healthcare management and Internet of Medical Things (IoMT) applications [37].”
Arslanoğlu, K., & Karaköse, M. (2025) · Diagnostics · doi:10.3390/diagnostics16010007 · Cited by 3
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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