PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
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Computer Science > Machine Learning
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Title:PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
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 federated training. PrivFusion uses agents to analyze local data, cluster semantically similar features across sites, and provide iterative transformation recommendations until alignment is achieved. Evaluation across four heterogeneous COVID-19 datasets demonstrates that PrivFusion effectively and efficiently harmonizes multi-site data while substantially reducing manual effort.
| Comments: | Accepted by IEEE CBMS 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.24249 [cs.LG] |
| (or arXiv:2605.24249v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24249
arXiv-issued DOI via DataCite (pending registration)
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