Practical training in electrocardiogram (ECG) interpretation remains uneven, particularly in resource-limited settings, despite the central role of ECGs in cardiovascular diagnosis. This work evaluates whether an ECG-focused digital twin that integrates interactive simulation and deep learning guidance can achieve educationally valid realism, improve recognition of patterns and abnormalities through interactivity, and enhance accuracy and learner motivation via predictive feedback. We present ECGTwinMentor, a cross-platform system that synthesizes parameterized ECG waveforms, enables fine-grained control of physiologic variables, and delivers immediate predictive feedback for formative assessment. The diagnostic model supports low-latency inference on modest hardware. Validation with healthcare experts and medical students showed positive evaluations for realism, usability, and integration potential. Experts reported average ratings between 3.5 and 4.5 out of 5, while students rated usability between 4.6 and 4.8 and motivation and realism at 5.0, with most items scoring at least 4. These findings support the conclusion that an interactive, predictive digital twin can narrow the gap between theory and practice in ECG interpretation, offering an accessible, scalable, and reproducible approach to ECG education.

Federated Learning has emerged as a promising approach for maintaining data privacy across distributed environments, enabling training on a diverse range of devices from high-performance servers to low-power gadgets. Despite its potential, managing numerous data sources can strain these devices, particularly those with limited capabilities, leading to increased latency. This is especially critical in virtual reality, where real-time responsiveness is crucial due to the need for constant data connectivity. Historically, virtual reality systems have relied on tethered computer setups, restricting their flexibility and the benefits of wireless technology. However, recent advancements have enhanced the computational power of VR devices, allowing them to perform certain tasks independently. This work explores the feasibility of training a neural network on VR devices, using a federated learning approach, to develop a collaborative model aggregated and stored in the cloud. The goal is to assess the computational demands and explore the potential and constraints of leveraging VR devices for artificial intelligence applications.