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

FLOP: Federated Learning on Medical Datasets using Partial Networks