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

arXiv: https://arxiv.org/abs/2505.20020 · HTML: https://mlsanity.com/paper/2505.20020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks](https://mlsanity.com/paper/2310.04270/) (2023)
- [Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents](https://mlsanity.com/paper/2405.10467/) (2024)
- [Differentially Private Decoding in Large Language Models](https://mlsanity.com/paper/2205.13621/) (2022)
- [Integration of Large Language Models and Federated Learning](https://mlsanity.com/paper/2307.08925/) (2023)
- [Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions](https://mlsanity.com/paper/2201.06505/) (2022)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models](https://mlsanity.com/paper/2407.00242/) (2024)
- [Interactive Data Harmonization with LLM Agents: Opportunities and Challenges](https://mlsanity.com/paper/2502.07132/) (2025)
- [Speaking the Same Language: Leveraging LLMs in Standardizing Clinical Data for AI](https://mlsanity.com/paper/2408.11861/) (2024)
- [Enhancing Health Data Interoperability with Large Language Models: A FHIR Study](https://mlsanity.com/paper/2310.12989/) (2023)

## Built on by (most cited first, 3 of 3)

- [LLM-Assisted Emergency Triage Benchmark: Bridging Hospital-Rich and MCI-Like Field Simulation](https://mlsanity.com/paper/2509.26351/) (2025)
- [Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study](https://mlsanity.com/paper/2510.09646/) (2025)
- [Federated Agent Optimization](https://mlsanity.com/paper/2610.01195/) (2026)

## Abstract (arXiv)

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.