  While developing artificial intelligence (AI)-based algorithms to solve
problems, the amount of data plays a pivotal role - large amount of data helps
the researchers and engineers to develop robust AI algorithms. In the case of
building AI-based models for problems related to medical imaging, these data
need to be transferred from the medical institutions where they were acquired
to the organizations developing the algorithms. This movement of data involves
time-consuming formalities like complying with HIPAA, GDPR, etc.There is also a
risk of patients' private data getting leaked, compromising their
confidentiality. One solution to these problems is using the Federated Learning
framework.
  Federated Learning (FL) helps AI models to generalize better and create a
robust AI model by using data from different sources having different
distributions and data characteristics without moving all the data to a central
server. In our paper, we apply the FL framework for training a deep learning
model to solve a binary classification problem of predicting the presence or
absence of COVID-19. We took three different sources of data and trained
individual models on each source. Then we trained an FL model on the complete
data and compared all the model performances. We demonstrated that the FL model
performs better than the individual models. Moreover, the FL model performed at
par with the model trained on all the data combined at a central server. Thus
Federated Learning leads to generalized AI models without the cost of data
transfer and regulatory overhead.
