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Towards efficient federated learning-based scheme in medical cyber-physical systems for distributed data
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-09-29 , DOI: 10.1002/spe.2894
Kehua Guo 1 , Nan Li 1 , Jian Kang 2 , Jian Zhang 1
Affiliation  

In recent years, Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) have made good progress in the medical field. The medical CPS (MCPS) based on AI can realize the efficient and reasonable utilization of medical resources and improve the quality of medical process. However, current MCPS are still facing several challenges, and the privacy protection of medical data is one of the most critical challenges. Since medical data is stored in different hospitals, most studies collect data from decentralized hospitals to train a disease diagnosis model, which is not conducive to the privacy protection of patients. And in some existing solutions, it is also difficult for doctors to select the optimal model from multiple models in clinical diagnosis. In this paper, we propose a novel scheme based on federated learning in MCPS for training disease diagnosis models from distributed medical image data. Our scheme is divided into three parts: the model provider, the server, and the consumer, and a detailed working process is designed for each part. This scheme can not only effectively solve the problem of privacy protection, but also solve the problem of model selection for doctors and save storage space. It can ensure that consumers automatically get a steadily improved disease diagnosis model. This scheme is performed on simulated distributed medical image datasets. The experimental results show the effectiveness and superiority of our scheme.

中文翻译:

面向分布式数据的医疗信息物理系统中基于联邦学习的高效方案

近年来,信息物理系统(CPS)和人工智能(AI)在医疗领域取得了良好的进展。基于人工智能的医疗CPS(MCPS)可以实现医疗资源的高效合理利用,提高医疗过程质量。然而,当前的 MCPS 仍然面临着几个挑战,医疗数据的隐私保护是最关键的挑战之一。由于医疗数据存储在不同的医院,大多数研究从分散的医院收集数据来训练疾病诊断模型,不利于患者的隐私保护。并且在现有的一些解决方案中,医生在临床诊断中也难以从多个模型中选择最优模型。在本文中,我们提出了一种基于 MCPS 联邦学习的新方案,用于从分布式医学图像数据中训练疾病诊断模型。我们的方案分为模型提供者、服务器和消费者三个部分,并为每个部分设计了详细的工作流程。该方案不仅可以有效解决隐私保护问题,还可以为医生解决模型选择问题,节省存储空间。它可以确保消费者自动获得一个稳步改进的疾病诊断模型。该方案是在模拟的分布式医学图像数据集上执行的。实验结果表明了我们方案的有效性和优越性。并为每一部分设计了详细的工作流程。该方案不仅可以有效解决隐私保护问题,还可以为医生解决模型选择问题,节省存储空间。它可以确保消费者自动获得一个稳步改进的疾病诊断模型。该方案是在模拟的分布式医学图像数据集上执行的。实验结果表明了我们方案的有效性和优越性。并为每一部分设计了详细的工作流程。该方案不仅可以有效解决隐私保护问题,还可以为医生解决模型选择问题,节省存储空间。它可以确保消费者自动获得一个稳步改进的疾病诊断模型。该方案是在模拟的分布式医学图像数据集上执行的。实验结果表明了我们方案的有效性和优越性。
更新日期:2020-09-29
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