Intelligent Community Health Literacy Enhancement Using Federated Natural Language Processing and Privacy-Preserving Deep Learning

Abstract

Community health literacy represents a critical determinant of population health outcomes, yet significant disparities persist across diverse demographic groups. This paper introduces FedHealth-NLP, a novel framework that combines Federated Learning (FL) with advanced Natural Language Processing (NLP) techniques to enhance community health literacy while preserving data privacy. Our approach employs a dual-model architecture integrating Federated BERT (FedBERT) for privacy-preserving health text understanding with a Personalized Knowledge Graph Neural Network (PKGNN) for context-aware health information recommendation. The framework enables decentralized learning across multiple community health organizations without requiring centralized data collection, addressing critical privacy concerns in health education. We introduce innovative mechanisms including differential privacy-enhanced gradient aggregation, adaptive personalization layers for culturally diverse communities, and a novel cross-lingual transfer learning module for multilingual health content delivery. Extensive experiments on four community health datasets spanning urban and rural populations demonstrate that FedHealth-NLP achieves 91.3% accuracy in health literacy assessment, 89.7% relevance in content recommendation, and reduces health information comprehension time by 34.2% compared to baseline approaches. Comprehensive parametric analysis reveals optimal configurations for federated aggregation strategies and NLP model architectures. The proposed framework offers significant implications for scalable, privacy-preserving health education interventions and addresses the digital divide in health literacy across underserved communities