  The outbreak of COVID-19 Disease due to the novel coronavirus has caused a
shortage of medical resources. To aid and accelerate the diagnosis process,
automatic diagnosis of COVID-19 via deep learning models has recently been
explored by researchers across the world. While different data-driven deep
learning models have been developed to mitigate the diagnosis of COVID-19, the
data itself is still scarce due to patient privacy concerns. Federated Learning
(FL) is a natural solution because it allows different organizations to
cooperatively learn an effective deep learning model without sharing raw data.
However, recent studies show that FL still lacks privacy protection and may
cause data leakage. We investigate this challenging problem by proposing a
simple yet effective algorithm, named \textbf{F}ederated \textbf{L}earning
\textbf{o}n Medical Datasets using \textbf{P}artial Networks (FLOP), that
shares only a partial model between the server and clients. Extensive
experiments on benchmark data and real-world healthcare tasks show that our
approach achieves comparable or better performance while reducing the privacy
and security risks. Of particular interest, we conduct experiments on the
COVID-19 dataset and find that our FLOP algorithm can allow different hospitals
to collaboratively and effectively train a partially shared model without
sharing local patients' data.
