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

• URL: http://arxiv.org/abs/2505.20020v1
• Date: Mon, 26 May 2025 14:09:17 GMT
• Title: Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare
• Authors: Natallia Kokash, Lei Wang, Thomas H. Gillespie, Adam Belloum, Paola Grosso, Sara Quinney, Lang Li, Bernard de Bono,
• Abstract summary: Federated learning (FL) enables collaborative modeling without sharing data, yet faces challenges harmonizing raw data in diverse clinical datasets.<n>This paper presents a two-step data alignment strategy integrating large models (LLMs) to support secure, privacy-preserving FL in healthcare.
• Score: 1.791002543005888
• License: http://creativecommons.org/licenses/by/4.0/
• 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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