We present a cryptographically verifiable federated learning architecture that enables cross-institutional clinical analytics without transmitting raw Electronic Health Records (EHR). Evaluated across 4 partner hospital nodes with epsilon-differential privacy bounds.
AIT: AIT-2026-00046
JIT: JIT-RESPO-02
DOI: 10.5555/respo.2026.00046
IIT: IIT-STAN-08 Healthcare Informatics
Federated Learning and Differential Privacy Protocols for Decentralized Healthcare Registries
Authors & Affiliations (1 Contributor)
Dr. Marcus Vance 1st Author
Clinical Assistant Professor of Biomedical Data Science • Stanford Center for Biomedical Informatics Research
Journal: RESPO Journal of Computational Science & Artificial Intelligence •
ISSN: 2990-1234 •
Vol. 4, Issue 2 (2026)
Received: Oct 4, 2026 | Published: 2026-10-04 07:35:01
Article Views
2,134
PDF Downloads
890
Citations
47
Article Index Rank
Q1 (Top 5%)
Article Impact Value
4.85
Journal Impact
6.42
Abstract
We present a cryptographically verifiable federated learning architecture that enables cross-institutional clinical analytics without transmitting raw Electronic Health Records (EHR). Evaluated across 4 partner hospital nodes with epsilon-differential privacy bounds.
Keywords:
federated learning, differential privacy, healthcare analytics, HIPAA compliance
Full Manuscript Content
Cite this Article (BibTeX Format)
Strictly formatted for LaTeX, Zotero, Mendeley, and Google Scholar bibliography import.
@article{admin2026_31,
title = {Federated Learning and Differential Privacy Protocols for Decentralized Healthcare Registries},
author = {admin},
journal = {RESPO Journal of Computational Science & Artificial Intelligence},
volume = {1},
number = {1},
pages = {1--10},
year = {2026},
doi = {10.5555/respo.2026.00046},
note = {AIT: AIT-2026-00046},
url = {https://respo.iledu.in/articles/federated-learning-and-differential-privacy-protocols-for-decentralized-healthcare-registries/}
}