| _version_ | 1866901526135439360 |
|---|---|
| author | JSETMS |
| author_facet | JSETMS |
| contents | <p>The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed the healthcare sector by enabling predictive analytics, early disease detection, and data-driven clinical decision-making. However, healthcare institutions often face challenges in sharing sensitive patient data due to privacy regulations, security risks, and ethical concerns. Traditional centralized machine learning approaches require aggregating data from multiple organizations, which increases the risk of data breaches and violates strict privacy policies. To address these issues, this study proposes a privacypreserving federated learning platform integrated with Large Language Model (LLM) automation for intelligent healthcare risk prediction. The proposed system allows multiple healthcare institutions to collaboratively train a global machine learning model without sharing raw patient data. Instead, each institution performs local model training and transmits only model parameters to a central aggregation server. These parameters are combined using the Federated Averaging algorithm to produce a generalized global predictive model while maintaining data privacy. Furthermore, the system incorporates a transformer-based LLM to generate human-readable explanations for prediction results, improving transparency and interpretability for medical professionals. The platform is implemented using a full-stack architecture consisting of a React-based frontend, Node.js backend, MongoDB database, and a Python Flask microservice for machine learning operations. The system also includes modules for authentication, dataset management, federated training coordination, model performance monitoring, and AI-driven explanation generation. Experimental results demonstrate that the proposed approach improves predictive accuracy while maintaining strict data privacy standards. The integration of federated learning and explainable AI provides a scalable and secure framework for collaborative healthcare analytics.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19149702 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | FEDERATED LEARNING WITH LLM AUTOMATION - WEB-BASED SYSTEM JSETMS <p>The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed the healthcare sector by enabling predictive analytics, early disease detection, and data-driven clinical decision-making. However, healthcare institutions often face challenges in sharing sensitive patient data due to privacy regulations, security risks, and ethical concerns. Traditional centralized machine learning approaches require aggregating data from multiple organizations, which increases the risk of data breaches and violates strict privacy policies. To address these issues, this study proposes a privacypreserving federated learning platform integrated with Large Language Model (LLM) automation for intelligent healthcare risk prediction. The proposed system allows multiple healthcare institutions to collaboratively train a global machine learning model without sharing raw patient data. Instead, each institution performs local model training and transmits only model parameters to a central aggregation server. These parameters are combined using the Federated Averaging algorithm to produce a generalized global predictive model while maintaining data privacy. Furthermore, the system incorporates a transformer-based LLM to generate human-readable explanations for prediction results, improving transparency and interpretability for medical professionals. The platform is implemented using a full-stack architecture consisting of a React-based frontend, Node.js backend, MongoDB database, and a Python Flask microservice for machine learning operations. The system also includes modules for authentication, dataset management, federated training coordination, model performance monitoring, and AI-driven explanation generation. Experimental results demonstrate that the proposed approach improves predictive accuracy while maintaining strict data privacy standards. The integration of federated learning and explainable AI provides a scalable and secure framework for collaborative healthcare analytics.</p> |
| title | FEDERATED LEARNING WITH LLM AUTOMATION - WEB-BASED SYSTEM |
| url | https://doi.org/10.5281/zenodo.19149702 |