# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
###### Abstract  
In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity presents significant challenges in developing a highly accurate deep learning (DL) global model for ECG classification, as traditional centralized approaches struggle to address privacy concerns, scalability issues, and model inconsistencies arising from diverse device characteristics. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without sharing raw data, thus preserving privacy and security. However, standard FL assumes uniform device capabilities and model architectures, which is impractical given the varied nature of ECG data collection devices. Although heterogeneity has been explored in other domains, its impact on ECG classification and the classification of similar time series physiological signals remains underexplored. In this study, we adopted HeteroFL, a technique that enables model heterogeneity to reflect real-world resource constraints. By allowing local models to vary in complexity while aggregating their updates, HeteroFL accommodates the computational diversity of different devices. This study evaluated the applicability of HeteroFL for ECG classification using the MIT-BIH Arrhythmia dataset, identifying both its strengths and limitations. Our findings establish a foundation for future research on improving FL strategies for heterogeneous medical data, highlighting areas for further optimization and adaptation in real-world deployments. federated learning; resource-aware computing; ECG classification; model heterogeneity +
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ited collaboration hinders generalization [4; 5; 6; 7]. These limitations highlight the need for techniques that can address these issues effectively. In 2016, Google researchers introduced Federated Learning (FL) [8] as a privacy-preserving solution to train models directly on edge devices, addressing challenges in data privacy and scalability. FL aggregates updates from local devices iteratively until the global model converges, enabling collaborative model training without sharing sensitive data. This approach is particularly suited for applications like ECG classification, where the data distribution is inherently non-identical across devices. By training models on local data, FL enhances data privacy and security while also improving the generalization of the global model by leveraging diverse patient data collected from different sources [9]. Traditional FL assumes that all devices collecting ECG data, such as wearables or smartphones, have similar computational capabilities and network conditions. Each device independently trains the same model using its local ECG data and then synchronously transmits the updated parameters to a central server. An aggregation algorithm, such as FedAvg [8; 10], combines these updates on the server after collecting all local model updates. While this synchronous aggregation ensures uniformity, it may struggle with efficiency and scalability, especially in resource-constrained environments where devices have limited computational power, battery life, and connectivity. Consequently, slower devices can delay global updates, leading to inefficient training and a reduced model convergence speed [11]. To overcome these limitations, several approaches, such as HeteroFL [12] and federated dropout [13], have been proposed to address the challenges posed by heterogeneous devices with varying computational power, memory, and network conditions, making them more suitable for real-world FL environments. Specifically, devices with different computational capacities train models of varying complexities, sending updates to the server asynchronously upon completing local training.