From Federated Learning to Real-Time Pneumonia Prediction: Deploying an AdaptiveMesh-Based Global Model on Resource-Constrained Devices - Shkurti, Lamir; Susuri, Arsim; Sofiu, Vehebi; Qehaja, Besnik (International Journal of Interactive Mobile Technologies (iJIM), 2026)
Federated learning (FL) enhances data privacy but faces significant deployment challenges on resource-constrained edge devices. Building on our previous development of the AdaptiveMesh algorithm, this study presents an end-to-end implementation of a resourceaware FL framework for pneumonia detection from chest X-ray images. The system is optimized for heterogeneous hardware and unstable network conditions. Validated on two independent datasets, the proposed global model achieved high diagnostic reliability, reaching up to 95% accuracy and critical recall performance for clinical use. A key contribution of this work is the practical transition from decentralized training to real-time prediction; we integrated the trained model into a lightweight Flask-based web application deployed on devices such as Raspberry Pi 4 and Intel Atom MiniPCs. Our results confirm that the AdaptiveMesh-based system provides a stable, privacy-preserving, and feasible solution for medical diagnostics in low-resource environments, bridging the gap between algorithmic optimization and real-world application.