Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare

Natallia Kokash; Lei Wang; Thomas H. Gillespie; Adam Belloum; Paola Grosso; Sara Quinney; Lang Li; Bernard de Bono

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arXiv · 2505.20020

Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare

Natallia Kokash·Lei Wang·Thomas H. Gillespie·Adam Belloum·Paola Grosso·Sara Quinney·Lang Li·Bernard de Bono

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Abstract

The rise of electronic health records (EHRs) has unlocked new opportunities for medical research, but privacy regulations and data heterogeneity remain key barriers to large-scale machine learning. Federated learning (FL) enables collaborative modeling without sharing raw data, yet faces challenges in harmonizing diverse clinical datasets. This paper presents a two-step data alignment strategy integrating ontologies and large language models (LLMs) to support secure, privacy-preserving FL in healthcare, demonstrating its effectiveness in a real-world project involving semantic mapping of EHR data.

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Natallia Kokash, Lei Wang, Thomas H. Gillespie, Adam Belloum, Paola Grosso, Sara Quinney, Lang Li, Bernard de Bono. 2025-05-26. Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare. https://arxiv.org/abs/2505.20020

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