Blockchain Technology and Neural Networks for the Internet of Medical Things
Abstract
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
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The proposed privacy-preserving federated learning scheme achieves an effectiveness average of 73.7%.
“the proposed scheme achieves an effectiveness average of 73,7%.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [104]
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Sadiq, M. S., Sıngh, I. P., & Ahmad, M. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00003-5 · Cited by 1 · see citing works
Next-Gen Healthcare: Blockchain-Fuelled Federated Learning for IoMT Security
Mohapatra, S., Mishra, A., & Mohanty, S. (2024) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp61711.2024.10870602 · Cited by 1 · see citing works
Unlocking Potential: An Analysis of Blockchain's Performance Impact on Business Transformation
Amla, M., & Bhatia, S. A. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83708-728-020251007 · Cited by 1 · see citing works
Security of Industrial Cyberspace: Fair Clustering with Linear Time Approximation
Chikhalia, N., & Dhawan, Y. (2021) · Springer eBooks · doi:10.1007/978-3-030-74753-4_5 · Cited by 1 · see citing works
Scalable Fair Clustering Algorithm for Internet of Things Malware Classification
Obuzor, Z., & Anidu, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-74753-4_18 · Cited by 1 · see citing works
Internet of Things use case applications for COVID-19
Nasajpour, M., Pouriyeh, S., Parizi, R. M., Zhao, L., & Li, L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90585-5.00016-3 · Cited by 1 · see citing works
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A secure and efficient multi-domain data sharing model on consortium chainPreprint
Zhang, W., Huo, X., & Bao, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-1578830/v2
Research on Composite Index Construction Method Based on Master-Slave Blockchain Structure
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A Smart Blockchain Model for Supply Chain Management System
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AI-enabled IoMT for smart cancer care: plausible use cases, key challenges, and future road map
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Blockchain-Assisted Federated Learning Framework for Data Processing in IoMT Domain
Mishra, A., Nayak, M., Priyadarsini, R., Mohanty, J. R., Yadav, A. S., & Barik, R. K. (2024) · IEEE International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC) · doi:10.1109/aespc63931.2024.10872355
Quantum-Secured Federated and Lottery Federated Learning for Privacy-Preserving AI
B, A. S. R., Karthika Renuka, D., & R, A. A. K. (2026) · International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) · doi:10.1109/iciscois62701.2026.11447994
- Authors
- Polap, D., Srivastava, G., Jolfaei, A., & Parizi, R. M.
- Affiliations
- Brandon University (CA)
- Macquarie University (AU)
- Kennesaw State University (US)
- Venue
- IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 508-513
- OpenAlex
- W3048637385
- Cited by
- 88
- References
- 23
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 15 · 2023: 13 · 2022: 15 · 2021: 28 · 2020: 4
- FWCI
- 11.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- blockchain, artificial neural networks, Internet of Medical Things, federated learning, decentralized learning, medical data privacy, model poisoning, remote patient monitoring, AI-assisted diagnosis
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