Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
This addresses privacy and class imbalance issues in clinical research, offering a novel alternative for federated learning, though it is incremental as it builds on existing methods like ABC and GMM.
The authors tackled the challenge of imbalanced classification in clinical data by developing a federated generative model that preserves privacy, achieving an F1 score boost to nearly ideal levels on the PhysioNet2012 dataset.
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.