ChestFed: Model-Contrastive Federated Learning for Cardiopulmonary Disease Classification in Chest X-rays using Multiple Datasets

Artificial intelligence in medical imaging has emerged to be a topic with high demand in medical practice in the recent years. However, limited data availability due to strict patient privacy policy becomes a main barrier in this area. Federated learning enables multiple parties to collaboratively train a machine learning/deep learning model without sharing their local data. Model-contrastive federated learning, as a novel federated learning framework, is designed to handle the heterogeneity of local data distribution by using contrastive learning across parties. In this work, we applied the model-contrastive federated learning in multiple chest x-ray datasets to derive a global model for disease diagnosis. Our experiment shows that using federated learning only on two datasets, our model outperforms the model trained in one single dataset by 4%, which indicates the potential to apply federated learning on several chest x-ray datasets to achieve higher accuracy without the need to share local data.