Abstract:
Data privacy is a major concern in data analysis due to ethical and legal considerations. To
overcome this issue, federated learning has transformed data analysis by putting data
privacy first throughout the model training process. We proposed a horizontal federated
learning system in this study after analyzing ECG data. Our methodology makes use of the
decentralized method of federated learning, in which local models are trained on ECG
datasets separately without the need for a central server. We collected data for our studies
using two publicly available ECG datasets: the PTB-XL and Chapman datasets.
Furthermore, we used a variety of preprocessing approaches to denoise the signals, as well
as different feature extraction techniques for both datasets to discover important
characteristics in the signals. In this method, the graph neural network (GNN) served as
the foundation for the local models. As a result, local models are trained separately on the
client side to create the global model for our proposed federated learning system. This
approach attempts to create an efficient federated learning model with high accuracy, as
well as a promising approach to data analysis in a variety of domains. Our experimental
findings underscore the usefulness of federated learning in sensitive data analysis by
showing that the suggested method achieves excellent accuracy while protecting data
privacy. Future research on safe and decentralized machine learning applications in several
domains is made possible by this work.
