Regulatory-orientedDeep federated learning framework for multi-hospital medical imaging: privacy-preserving, explainable, and generalizable diagnosis

Abstract

Medical imaging has been transformed by Artificial Intelligence (AI) and Deep Learning (DL). Yet, multi-hospital deployment remains limited by patient privacy concerns, heterogeneous data distributions, and insufficient model interpretability, which affect regulatory approval and clinical trust. This study proposes a regulatory-grade Federated Learning (FL) framework for secure, interpretable, and generalizable collaborative medical imaging. The proposed framework integrates Slicing Window Adaptive Kalman Filtering (SWAKF) for image denoising, Structured Multi-Modal Autoencoder Attention Fusion (SMAAF) for feature representation, and adaptive federated aggregation to address non-IID data across hospitals. Patient privacy is preserved using secure aggregation, differential privacy, and encryption, while Grad-CAM, SHAP, and LIME provide model interpretability. The proposed framework outperformed Vision Transformer, AlexNet, FedAvg, and FedProx on Brain Tumor and Alzheimer's MRI datasets. It achieved 96.1% accuracy F1-score 96.1%, and 0.978 for Brain Tumor classification, and 94.8% accuracy and 0.968 for Alzheimer's classification. The framework also reduced calibration error, exhibited minimal encryption overhead, maintained robustness under noisy-label and non-IID conditions, and demonstrated statistically significant improvements p < 0.01 over baseline methods. The proposed FL framework provides a privacy-preserving, explainable, and computationally efficient solution for collaborative AI in medical imaging. By combining adaptive federated learning, secure privacy mechanisms, and explainable AI techniques, it improves diagnostic performance while supporting regulatory compliance and clinical trust, demonstrating strong potential for deployment in multi-hospital clinical environments.