A Federated Learning Paradigm for Heart Sound Classification
A Federated Learning Paradigm for Heart Sound Classification
复制标题
DOI：
10.1109/embc48229.2022.9871319
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
Wanyong Qiu;Kun Qian;Zhihua Wang;Yi Chang;Zhihao Bao;B. Hu;B. Schuller;Yoshiharu Yamamoto
中科院分区：
文献类型：
--
作者：
Wanyong Qiu;Kun Qian;Zhihua Wang;Yi Chang;Zhihao Bao;B. Hu;B. Schuller;Yoshiharu Yamamoto
心血管疾病(CVD)已被列为主要的死亡原因。心血管疾病的早期诊断是医学实践中的一项重要任务。人们对心音的自动听诊进行了大量的努力，利用计算机听力的力量开发了一种廉价的、非侵入性的方法，可以随时随地使用来测量心脏的状态。然而，以往的研究忽略了一个重要的因素，即用户数据的隐私。一方面，学习过的模型总是渴望获得更大的数据。另一方面，在收集如此大量的数据时，可能很难保护个人隐私信息。在这种情况下，我们提出了一种用于心音分类任务的联邦学习(FL)框架。据我们所知，这是第一次将外语引入这一领域。我们进行了多个实验，分析了跨协作机构的数据分布对模型质量和学习模式的影响，并基于真实数据验证了FL的可行性和有效性。通过增加全局共享数据的策略，可以有效地提高非独立同分布(Non-IID)数据和模型的质量。
Cardiovascular diseases (CVDs) have been ranked as the leading cause for deaths. The early diagnosis of CVDs is a crucial task in the medical practice. A plethora of efforts were given to the automated auscultation of heart sound, which leverages the power of computer audition to develop a cheap, non-invasive method that can be used at any time and anywhere for measuring the status of the heart. Nevertheless, previous works ignore an important factor, namely, the privacy of the user data. On the one hand, learnt models are always hungry for bigger data. On the other hand, it can be difficult to protect personal private information when collecting such large amount of data. In this dilemma, we propose a federated learning (FL) framework for the heart sound classification task. To the best of our knowledge, this is the first time to introduce FL to this field. We conducted multiple experiments, analysed the impact of data distribution across collaborative institutions on model quality and learning patterns, and verified the feasibility and effectiveness of FL based on real data. Non- independent identically distributed (Non-IID) data and model quality can be effectively improved by adding a strategy of globally sharing data.