  COVID-19 pandemic has spread rapidly and caused a shortage of global medical
resources. The efficiency of COVID-19 diagnosis has become highly significant.
As deep learning and convolutional neural network (CNN) has been widely
utilized and been verified in analyzing medical images, it has become a
powerful tool for computer-assisted diagnosis. However, there are two most
significant challenges in medical image classification with the help of deep
learning and neural networks, one of them is the difficulty of acquiring enough
samples, which may lead to model overfitting. Privacy concerns mainly bring the
other challenge since medical-related records are often deemed patients'
private information and protected by laws such as GDPR and HIPPA. Federated
learning can ensure the model training is decentralized on different devices
and no data is shared among them, which guarantees privacy. However, with data
located on different devices, the accessible data of each device could be
limited. Since transfer learning has been verified in dealing with limited data
with good performance, therefore, in this paper, We made a trial to implement
federated learning and transfer learning techniques using CNNs to classify
COVID-19 using lung CT scans. We also explored the impact of dataset
distribution at the client-side in federated learning and the number of
training epochs a model is trained. Finally, we obtained very high performance
with federated learning, demonstrating our success in leveraging accuracy and
privacy.
