# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 6 Discussions  
Through extensive experimentation on highly imbalanced ECG data, we have demonstrated that HeteroFL effectively accommodates device heterogeneity in FL settings. Our results indicate that multi-model configurations enable a diverse range of ECG-enabled wearable devices to collaboratively train a global model, allowing both high-end and resource-constrained devices to contribute meaningfully. This is particularly significant as it suggests that FL can facilitate broad participation across devices with varying computational capacities, thereby enhancing accessibility and inclusivity in medical AI applications.  
A key insight from our study is that the meticulous selection of model configurations plays a crucial role in optimizing performance. While larger models provide stronger global performance, smaller models still contribute meaningfully, and their inclusion can lead to a more balanced and robust learning process. However, imbalanced model contributions pose a challenge, as weaker devices with fewer parameters introduce updates that may slow convergence. Our findings suggest that strategic configurations--where smaller models are paired with appropriately sized larger models--can mitigate these effects and improve the overall performance.  
Moreover, our analysis highlights that heterogeneous FL settings can achieve competitive accuracy while maintaining computational efficiency, making them a viable approach for real-world ECG classification. The results encourage further exploration into adaptive strategies that dynamically adjust model configurations based on the available resources and data characteristics, potentially leading to more efficient and scalable FL systems for physiological signal processing.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Ratio** & **Parameters** & **Acc., Global** & **Acc., Local** & **F1 Score,** &
\begin{tabular}{l} **F1 Score,** \\ **Global** \\ \end{tabular} \\ \hline a & 1 & 267,464 & 99.28 & 99.21 & 0.9735 & 0.9987 \\ a-e & 0.5 & 160,973 & 92.89 & 96.17 & 0.7053 & 0.8131 \\ a  
Despite its advantages, our study reveals two key limitations of HeteroFL in its current form. First, the current implementation of HeteroFL relies on a fixed number of model variants (i.e., five configurations: a, b, c, d, and e). This rigid structure limits flexibility in real-world deployments, where devices may have a more continuous spectrum of computational capabilities. A more dynamic approach that automatically adjusts the number and complexity of model variants could enhance adaptability and efficiency. Second, as the number of participating devices increases, the aggregation process becomes more complex, and disparities in model sizes can cause slower convergence and potential performance degradation. Efficient aggregation mechanisms that adaptively weigh contributions from heterogeneous models will be necessary to ensure that the system scales effectively while maintaining high performance across diverse device capabilities.  
By addressing these limitations, future work can enhance the practicality of HeteroFL for large-scale, real-world ECG classification tasks, paving the way for more robust and adaptive FL frameworks in medical AI applications.