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.

翻译：心血管疾病是全球最主要的死亡原因，早期检测可显著改善患者预后。机器学习模型有助于早期诊断心血管疾病，但模型训练可用数据限制了其性能。医疗领域的隐私担忧使得获取数据以训练精确的机器学习模型变得更加困难。联邦学习是一种新兴的机器学习方法，它允许在多个数据源上训练模型，同时不损害个体数据所有者的隐私。本综述论文概述了当前联邦学习用于心血管疾病检测的最新进展。我们回顾了不同论文中提出的各类联邦学习模型，并探讨了它们的优势与挑战。同时，我们将联邦学习与传统集中式学习方法进行比较，突出其在模型准确性、隐私保护和数据分布处理能力方面的差异。最后，我们对当前联邦学习在心血管疾病检测中的挑战与局限性进行了批判性分析，并探讨了未来研究的可能方向。总体而言，本综述旨在全面展示联邦学习在心血管疾病检测中的最新研究现状，并强调其在提升心血管疾病检测模型准确性与隐私保护能力方面的潜力。