  Artificial Intelligence-based (AI) analysis of large, curated medical
datasets is promising for providing early detection, faster diagnosis, and more
effective treatment using low-power Electrocardiography (ECG) monitoring
devices information. However, accessing sensitive medical data from diverse
sources is highly restricted since improper use, unsafe storage, or data
leakage could violate a person's privacy. This work uses a Federated Learning
(FL) privacy-preserving methodology to train AI models over heterogeneous sets
of high-definition ECG from 12-lead sensor arrays collected from six
heterogeneous sources. We evaluated the capacity of the resulting models to
achieve equivalent performance compared to state-of-the-art models trained in a
Centralized Learning (CL) fashion. Moreover, we assessed the performance of our
solution over Independent and Identical distributed (IID) and non-IID federated
data. Our methodology involves machine learning techniques based on Deep Neural
Networks and Long-Short-Term Memory models. It has a robust data preprocessing
pipeline with feature engineering, selection, and data balancing techniques.
Our AI models demonstrated comparable performance to models trained using CL,
IID, and non-IID approaches. They showcased advantages in reduced complexity
and faster training time, making them well-suited for cloud-edge architectures.
