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Traceability in Federated Learning in Healthcare
Fu-Sung Kim-Benjamin Tang1, Ana Grönke2, German Sergei3
1Institute for Biomedical Informatics, University of Cologne, Medical Faculty and University Hospital Cologne, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Federated learning (FL) enhances healthcare data privacy but lacks transparency. This review finds blockchain is key for traceability in FL, though healthcare applications and evaluation frameworks need development.
Area of Science:
- Health Informatics
- Computer Science
- Data Science
Background:
- Federated learning (FL) offers privacy-preserving analytics for distributed healthcare data.
- Transparency and accountability are crucial for trust in healthcare AI but remain challenging in FL.
- Traceability is a key component of transparency in federated learning systems.
Purpose of the Study:
- To systematically review and assess the state of traceability in federated learning within the healthcare domain.
- To identify current approaches and challenges in achieving transparency through traceability in healthcare FL.
Main Methods:
- A scoping review following the PRISMA-ScR methodology.
- Systematic screening of 125 articles across four databases.
- Inclusion criteria applied to select 53 relevant studies.
Main Results:
- 77.36% of reviewed articles utilize blockchain technology to enhance traceability.
- A limited number of studies focus specifically on healthcare applications of FL traceability.
- Comprehensive evaluation frameworks for traceability in healthcare FL are notably absent.
Conclusions:
- Blockchain integration shows promise for improving traceability, trust, and auditability in healthcare FL.
- Further research is needed to develop robust traceability mechanisms and evaluation frameworks tailored for healthcare FL.
- Enhancing traceability is essential for accountability and clinical adoption of federated learning in healthcare.
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