XMF-FL: An Explainable Multimodal Feature Fusion Federated Learning Framework for Multi-Class Kidney Disease Classification

We proposed an explainable multimodal feature fusion FL framework for privacy-aware multi-class kidney disease classification. The framework integrates a fine-tuned transfer learning model (i.e., FT-EfficentNetB0) and a customized CNN model using a multimodal dataset (i.e., kidney CT images and corresponding clinical data) in a heterogeneous environment to enhance the
performance of kidney disease. In addition, the framework explains predictions through XAI methods to provide accurate, trustworthy, and privacy-preserving kidney disease diagnosis.

The key contributions of this study are presented as follows:
1) We propose an explainable multimodal feature fusion federated learning framework called XMF-FL for classifying multi-class kidney diseases into four categories while preserving patient privacy.
2) We implemented a heterogeneous data distribution simulation environment with multiple healthcare clients to ensure the performance of the framework in a real clinical environment.
3) The Explainable AI (XAI) integrates with the framework to identify key imaging and clinical features, enhance model transparency, and ensure the trustworthiness of clinicians.
4) We compared the proposed XMF-FL framework with
the state-of-the-art models to validate the effectiveness.