Applying a Task-Based Approach to Distributed Machine Learning Workflows

The growing demands across various scientific fields have led to a significant shift in applications that consume data at the edge of the computing continuum. These applications require unified programming models for the composition of components and coordinating the execution of computational workloads, including training machine learning (ML) models on distributed resources. Personalized healthcare often leverages data generated from wearable devices used to train ML models, can be benefited from distributed computing approaches. Specifically, stroke care can be greatly benefited from distributed ML with modifiable risk factors that can be monitored using wearable devices. In this work, we present an implementation that leverages distributed techniques for large-scale ML workflows using electrocardiogram (ECG) recordings for atrial fibrillation (AF) classification. The application was evaluated using the PhysioNet database, showcasing the potential of distributed, ML in stroke care, opening the way for future creation of more advanced models embedded in edge devices.