# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 5 Results  
In Figure 4, we show the performance of two-model combinations under i.i.d settings. As the number of parameters decreased in combinations such as 'a-e', 'b-e', and 'c-e', the global model's accuracy did not maintain the same high levels as in models a, b, and c alone, which achieved accuracies of 99%, 98%, and 98%, respectively. An exception was observed in the combination with model d, where the performance of d-e reached 97.6%, matching the accuracy of model d. In these combinations, only the aggregated global models ('a', 'b', 'c', or 'd') were used during testing. Consequently, smaller models ('e') trained by weak learners could be tested with the larger models ('a', 'b', 'c', or 'd'), achieving performance above 95%. This was still better than the global accuracy of the single model 'e', which was 94.8%. Figure 4: Global Test Accuracy per round for different heterogeneous local clients. Figure 4 also illustrates that larger models, such as 'a' and 'b', exhibited faster convergence due to their higher parameter count and greater representational capacity, which enabled them to learn patterns more efficiently during training. In contrast, smaller models like 'e', which have fewer parameters and lower complexity, converged more slowly as they required more iterations to achieve comparable performance. Additionally, in heterogeneous settings where model 'e' was combined with larger models, the global model's convergence was slowed. This occurred because the smaller model introduced weaker updates into the aggregation process, reducing the overall pace of optimization. The disparity in the model capacities created imbalances in the contributions of local updates, causing the global model to require more rounds to stabilize and converge effectively. In terms of the global model accuracy, homogeneous settings consistently achieved the highest accuracy, as all models shared the same architecture and capacity, allowing for the more uniform and efficient aggregation of updates. In contrast, heterogeneous settings tended to show lower global accuracy, as the variation in the model capacities and complexities led to less effective aggregation. Smaller models in heterogeneous settings may contribute less informative updates, resulting in a global model that cannot leverage the full potential of the larger models. Moreover, the accuracy drop became more pronounced as the difference in the model sizes increased. When the disparity between the sizes of the local models was large, the small models' contributions became more detrimental to the global model's performance, further reducing the overall accuracy. This highlights the challenge of balancing the model capacities in heterogeneous environments to maintain optimal global performance. In Table 4, we present the results for the i.i.d. data for the multi-model combination performance. We hierarchically show the number of model parameters, as well as the global and local model accuracies, along with the F1 score for each model setting. As the parameter count decreased, the model performance was also reduced. The largest model combined with relatively smaller models, such as 'a-b-c-d-e', yielded a reasonable global model accuracy of 98%, compared to the homogeneous global accuracy of model 'a', which was 99%. In the 'b' class, the multi-model combination 'b-c-d-e' achieved an accuracy of 97%, which was a slight reduction from the largest model's accuracy of 99%. In the 'c-d-e' combination, the model achieved a global accuracy of 97%, which was close to the performance of the largest model in this class, model 'c', with an accuracy of 98%. This combination also yielded a reasonable F1 score, which was calculated based on the final global model accuracy. In all cases, the smaller models 'd' and 'e' showed improved performance compared to their own model class performances, which were 97% and 94%, respectively.