  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.
