Blockchain-Based Personalized Federated Learning for Internet of Medical Things
Abstract
The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.
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The collection of federated learning with blockchain and privacy-preserving techniques such as privacy-enhancing technology strengthens data protection capabilities, which can be instrumental for machine learning in highly regulated healthcare sectors.
“Furthermore, the collection of FL with blockchain and privacy-preserving techniques such as privacy-enhancing technology further strengthens data protection capabilities, which can be instrumental for the utilization of ML in highly regulated healthcare sectors [28].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [28]
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- Authors
- Lian, Z., Wang, W., Han, Z., & Su, C.
- Affiliations
- University of Aizu (JP)
- City University of Hong Kong (HK)
- Nanjing Forestry University (CN)
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2023-10
- Volume
- 8
- Issue
- 4
- Pages
- 694-702
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- OpenAlex
- W4377717231
- Cited by
- 75
- References
- 47
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 36 · 2023: 6
- FWCI
- 6.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Computing (publishedVersion)
- Funders
- JSPS Grant-in-Aid for Scientific Research (23K11103)
- NEC C&C Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- blockchain, personalized federated learning, Internet of Medical Things, patient data privacy, decentralized learning, edge computing, personalized healthcare, data security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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