  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.
