Design and Implementation of a Federated Learning System for Digital Twin-Metaverse Convergence

The integration of federated learning (FL), digital twins, and the metaverse represents a transformative step toward addressing challenges in decentralized systems, such as data privacy, computational efficiency, and real-time interaction. This paper presents a novel framework that combines these technologies to enable seamless collaboration between physical and virtual environments. Using real-world datasets like MIMIC-III and synthetic IoT data, we demonstrate the framework's effectiveness in achieving high accuracy (96.5%), reducing privacy leakage ($\epsilon=0.5$), and minimizing computational overhead and latency. The proposed solution supports scalability for up to 100 devices, outperforming prior benchmarks, and leverages tools like Microsoft Azure Digital Twins, TensorFlow Federated, and Unity 3D. Experimental results highlight significant improvements in healthcare and industrial IoT applications. By bridging the gap between physical and virtual worlds, this framework paves the way for advancements in smart factories, remote healthcare, and beyond, offering a robust foundation for future innovation.