# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 2 Related Work  
Machine learning (ML) techniques have been widely applied to ECG classification, with methods such as the Support Vector Machine (SVM) [19] and Least Square Twin SVM with k-Nearest Neighbors (kNNs) [20] used for arrhythmia detection. Early ML-based methods also included techniques like fuzzy C-means clustering, the Mahalanobis distance, and abstract feature extraction [21], as well as hybrid models such as the Long Short-Term Memory-Support Vector Machine (LSTM-SVM) [22]. While these approaches provided initial success, they struggled with accurately interpreting ECG features and modeling the complex interrelationships in high-dimensional data, limiting their overall effectiveness. To overcome the limitations of ML classifiers, researchers adopted deep learning (DL) models, particularly Convolutional Neural Networks (CNNs). Notable work included CNN-based heartbeat detection [23], optimized CNN architectures using bat-rider algorithms [24], and deep multiscale fusion models that integrate diverse convolutional kernels [25]. Hybrid architectures, such as CNN-LSTM models [26] and subject-adaptive learning frameworks [27], have further improved ECG classification accuracy. However, these approaches require substantial computational resources and large-scale labeled datasets, making them unsuitable for deployment on real-time, resource-constrained wearable devices. With the increasing need for decentralized data privacy-preserving solutions, FL has emerged as a promising approach for ECG classification. FL enables large-scale model training across multiple clients while ensuring that sensitive patient data remain on local devices. Since 2021, numerous FL strategies have been proposed to address the challenges of ECG data distribution and privacy. Asynchronous Federated Learning (Async-FL) [28] was introduced to reduce the communication bandwidth and improve model convergence by allowing for independent client updates, effectively handling non-IID (non-identically distributed) ECG data and enhancing the arrhythmia detection efficiency. Federated Cluster (FedCluster) [29] improved classification accuracy by clustering clients based on similar data distributions, mitigating non-IID effects and benefiting devices with skewed datasets. Another study [30] introduced an FL-based healthcare framework integrating explainable AI and deep CNNs while employing communication cost reduction techniques to enhance privacy. More recent advancements, such as Federated Echocardiography (FedECG) [31], leveraged ResNet-9 with labeled ECG data, demonstrating the feasibility of semi-supervised learning in federated settings. A comparative study [32] analyzed the performance of centralized and federated models on the MIT-BIH Arrhythmia dataset, confirming that FL can maintain high accuracy even in real-world decentralized scenarios. While these studies significantly improved FL-based ECG classification, they primarily focused on model accuracy and privacy without addressing the critical issue of device heterogeneity. In real-world FL deployments, client devices have vastly different computational capacities, limiting the efficiency and scalability of existing solutions. To tackle device heterogeneity in FL, various strategies have been proposed in other domains, primarily focusing on image and text datasets. HeteroFL [12] mitigates variability in client resources by employing output channel pruning and layer-wise aggregation, ensuring that devices with different computing capacities can still participate effectively. Split-Mix [33] and adaptive FL [34] optimize training by dynamically adjusting model partitions based on the client-server configuration. Other approaches, such as federated edge learning [35] and low-rank model fusion in FL in Heterogeneous Models (FEDHM) [36], enhance FL performance in distributed environments.