Akademik Veri Yönetim Sistemi
7th International Conference on Smart Applications, Communications and Networking, SmartNets 2025, Hybrid, Istanbul, Türkiye, 22 - 24 Temmuz 2025, (Tam Metin Bildiri)
In recent years, significant advances have been made in health systems with the widespread adoption of the Internet of Things (IoT) and AI-powered innovative services. IoT enables the continuous collection of health data, while Artificial Intelligence (AI) analyzes this data to enhance early disease diagnosis. However, traditional Deep Learning (DL) approaches generally rely on centralized systems that collect and process personal data, posing a risk of violating individual privacy. In this study, a Federated Learning (FL) approach is used to process health data obtained from wearable smart devices while preserving privacy and classifying individuals' health scores. To overcome the performance limitations of FL approaches, a novel approach, FedSync, is proposed to incorporate a parallel training mechanism. To demonstrate the superiority of the FedSync approach, results are compared with DL and FL approaches in terms of accuracy, precision, recall, and F1-score. According to the results, the proposed approach provides a 29.02% improvement in computational time performance compared to FL and a 21.01% improvement compared to DL. Furthermore, to address the lack of explainability in IoT data, Explainable AI (XAI) has been integrated into the experiments to identify the most influential features affecting the classification score. This study makes a significant contribution to the literature by presenting a synchronous FL approach supported by XAI for IoT-based health data analysis.