Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review

TL;DR

This paper reviews federated learning's potential and challenges in improving cardiovascular disease detection by enabling privacy-preserving, multi-source data training, and compares it with traditional methods.

Contribution

It provides a comprehensive overview of federated learning applications in CVD detection, highlighting advantages, challenges, and future research directions.

Findings

Federated learning enhances privacy in CVD detection models.

FL can achieve comparable accuracy to centralized learning.

Current challenges include data heterogeneity and model convergence issues.

Abstract

Cardiovascular diseases (CVD) are the leading cause of death globally, and early detection can significantly improve outcomes for patients. Machine learning (ML) models can help diagnose CVDs early, but their performance is limited by the data available for model training. Privacy concerns in healthcare make it harder to acquire data to train accurate ML models. Federated learning (FL) is an emerging approach to machine learning that allows models to be trained on data from multiple sources without compromising the privacy of the individual data owners. This survey paper provides an overview of the current state-of-the-art in FL for CVD detection. We review the different FL models proposed in various papers and discuss their advantages and challenges. We also compare FL with traditional centralized learning approaches and highlight the differences in terms of model accuracy, privacy, and data distribution handling capacity. Finally, we provide a critical analysis of FL's current challenges and limitations for CVD detection and discuss potential avenues for future research. Overall, this survey paper aims to provide a comprehensive overview of the current state-of-the-art in FL for CVD detection and to highlight its potential for improving the accuracy and privacy of CVD detection models.