Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance
Yahya Shahsavari, Yaser Baseri, Abdelhakim Hafid, Oussama Abderrahmane Dambri, Dimitrios Makrakis, Andrew Coristine · 2026
Read Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance by Yahya Shahsavari, Yaser Baseri, Abdelhakim Hafid, Oussama Abderrahmane Dambri, Dimitrios Makrakis, Andrew Coristine (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13268642.txt.
Description
Yahya Shahsavari and colleagues present a 2026 tutorial on joining federated learning and blockchain for health care data. Hospitals want to train shared AI models without pooling raw patient data. Federated learning does that, training locally and sharing only model updates, but it can be poisoned or leak information. Blockchain, a tamper-proof shared ledger, can add trust and audit trails.
The tutorial maps medical data types and their learning needs. It compares three ways to couple the two technologies, from tightly fused to loosely linked, weighing security, scale, and rule compliance. It reviews attacks specific to health care and defenses such as zero-knowledge proofs and privacy noise. A compliance framework covers US and European privacy law and device rules. Uses range from disease prediction to drug discovery.
Catalog record
| CREATOR | Yahya Shahsavari, Yaser Baseri, Abdelhakim Hafid, Oussama Abderrahmane Dambri, Dimitrios Makrakis, Andrew Coristine |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 181843 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |