Secure Federated Learning Approaches to Diagnosing COVID-19
Rittika Adhikari Christopher Settles
The recent pandemic has underscored the importance of accurately diagnosing
COVID-19 in hospital settings. A major challenge in this regard is
differentiating COVID-19 from other respiratory illnesses based on chest
X-rays, compounded by the restrictions of HIPAA compliance which limit the
comparison of patient X-rays. This paper introduces a HIPAA-compliant model to
aid in the diagnosis of COVID-19, utilizing federated learning. Federated
learning is a distributed machine learning approach that allows for algorithm
training across multiple decentralized devices using local data samples,
without the need for data sharing. Our model advances previous efforts in chest
X-ray diagnostic models. We examined leading models from established
competitions in this domain and developed our own models tailored to be
effective with specific hospital data. Considering the model's operation in a
federated learning context, we explored the potential impact of biased data
updates on the model's performance. To enhance hospital understanding of the
model's decision-making process and to verify that the model is not focusing on
irrelevant features, we employed a visualization technique that highlights key
features in chest X-rays indicative of a positive COVID-19 diagnosis.