arXiv:2605.24249v1 Announce Type: new Abstract: The growing availability of clinical data has increased the use of machine learning, yet centralized data aggregation is often infeasible for sensitive health information. Federated Learning (FL) offers a distributed alternative, but its adoption is limited by substantial heterogeneity across institutional datasets, making harmonization a critical but frequently overlooked prerequisite for multi-site analytics. We introduce PrivFusion, a privacy-preserving multi-agent framework that automates the harmonization of structured datasets prior to fede
The proliferation of clinical data and the growing need for multi-site analytics, coupled with privacy concerns, necessitates robust solutions like PrivFusion to bridge the gap between data availability and secure utilization.
This framework directly addresses a critical barrier to deploying machine learning in sensitive domains like healthcare, enabling broader and more effective use of distributed data without centralizing personal information.
The ability to harmonize heterogeneous datasets across multiple institutions in a privacy-preserving manner facilitates previously challenging cross-institutional research and AI model training.
- · Healthcare research institutions
- · AI developers in healthcare
- · Patients (through improved diagnostics/therapeutics)
- · Data privacy solution providers
- · Centralized data aggregators
- · Organizations with poor data governance
- · Ransomware attackers (as less central data to target)
PrivFusion enables more effective multi-institutional federated learning applications, particularly in healthcare.
Improved data sharing and collaboration could accelerate drug discovery, personalized medicine, and population health initiatives.
The success of privacy-preserving harmonization in healthcare could set a precedent for other sensitive data domains, fostering broader adoption of distributed AI.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG