  Federated Learning (FL) is a suitable solution for making use of sensitive
data belonging to patients, people, companies, or industries that are
obligatory to work under rigid privacy constraints. FL mainly or partially
supports data privacy and security issues and provides an alternative to model
problems facilitating multiple edge devices or organizations to contribute a
training of a global model using a number of local data without having them.
Non-IID data of FL caused from its distributed nature presents a significant
performance degradation and stabilization skews. This paper introduces a novel
method dynamically balancing the data distributions of clients by augmenting
images to address the non-IID data problem of FL. The introduced method
remarkably stabilizes the model training and improves the model's test accuracy
from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images
in highly non-IID FL setting. The results of IID, non-IID and non-IID with
proposed method federated trainings demonstrated that the proposed method might
help to encourage organizations or researchers in developing better systems to
get values from data with respect to data privacy not only for healthcare but
also other fields.
