Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces huge communication cost of transferring model updates and can hardly ensure model performance when data heterogeneity of clients heavily exists. To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyse medical diagnostic images. Further, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion-based on participating clients' training time. In addition, we summarise a category of medical diagnostic image datasets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency and fault tolerance.

中文翻译：

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用于 COVID-19 检测的基于动态融合的联邦学习

使用机器学习的医学诊断图像分析（例如 CT 扫描或 X 射线）是检测 COVID-19 感染的有效且准确的方法。但是，出于对患者隐私的考虑，通常不允许跨医疗机构共享诊断图像。这会导致用于训练图像分类模型的数据集不足的问题。联邦学习是一种新兴的隐私保护机器学习范式，它根据接收到的客户端训练的本地模型的更新来生成无偏的全局模型，而无需交换客户端的本地数据。然而，联邦学习的默认设置引入了传输模型更新的巨大通信成本，并且在客户端数据异构性严重存在的情况下难以保证模型性能。为了提高通信效率和模型性能，在本文中，我们提出了一种新的基于动态融合的联邦学习方法，用于医学诊断图像分析以检测 COVID-19 感染。首先，我们为基于动态融合的联邦学习系统设计了一种架构来分析医学诊断图像。此外，我们提出了一种动态融合方法，根据参与客户的本地模型性能动态决定参与客户，并根据参与客户的训练时间安排模型融合。此外，我们总结了一类用于 COVID-19 检测的医学诊断图像数据集，可供机器学习社区用于图像分析。