  In clinical research, the lack of events of interest often necessitates
imbalanced learning. One approach to resolve this obstacle is data integration
or sharing, but due to privacy concerns neither is practical. Therefore, there
is an increasing demand for a platform on which an analysis can be performed in
a federated environment while maintaining privacy. However, it is quite
challenging to develop a federated learning algorithm that can address both
privacy-preserving and class imbalanced issues. In this study, we introduce a
federated generative model learning platform for generating samples in a
data-distributed environment while preserving privacy. We specifically propose
approximate Bayesian computation-based Gaussian Mixture Model called 'Federated
ABC-GMM', which can oversample data in a minor class by estimating the
posterior distribution of model parameters across institutions in a
privacy-preserving manner. PhysioNet2012, a dataset for prediction of mortality
of patients in an Intensive Care Unit (ICU), was used to verify the performance
of the proposed method. Experimental results show that our method boosts
classification performance in terms of F1 score up to nearly an ideal
situation. It is believed that the proposed method can be a novel alternative
to solving class imbalance problems.
