Decentralized Mobile Diagnostic Framework for Dengue Utilizing Heterogeneous Federated Transfer Learning and Edge Transpilation

The escalating severity of Dengue fever outbreaks in Bangladesh necessitates rapid, data-driven clinical diagnostic tools. While recent machine learning architectures demonstrate high predictive accuracy using clinical and meteorological data, their reliance on centralized cloud infrastructure and heavy Python backends introduces severe data privacy vulnerabilities and renders them impractical for low-bandwidth, rural healthcare centers. This paper proposes a decentralized, privacypreserving mobile diagnostic framework utilizing Heterogeneous Federated Transfer Learning (HFTL). To the best of our knowledge, this is the first federated framework for dengue detection to collaboratively synthesize three distinct data modalities: haematological parameters, subjective symptomatic profiles, and environmental indicators. Furthermore, we introduce a novel edge-transpilation pipeline that mathematically converts trained balanced Random Forest classifiers directly into zero-dependency, native Dart code. This approach completely decouples diagnostic inference from the cloud, enabling zero-latency, 100% offline execution directly on mobile edge devices. Experimental evaluations confirm exceptional diagnostic performance, achieving 96.0% accuracy on structural symptom vectors and 76.4% on haematological markers. By ensuring that sensitive patient health data never leaves the local client device, this framework bridges the critical gap between robust computational epidemiology and secure, deployable digital health infrastructure in resourceconstrained environments.