Federated deep learning for stroke detection: a privacy-preserving, explainable CNN-LSTM framework.

Strict privacy regulations in healthcare limit the centralized sharing of medical imaging data, creating challenges for developing robust and generalizable diagnostic models. Federated learning (FL) offers a privacy-preserving paradigm in which participating institutions collaboratively train a global model without directly exchanging patient images. This study presents a federated CNN-LSTM framework for privacy-preserving brain stroke classification from MRI scans, designed to learn discriminative spatial and sequential representations while retaining training data at the local client level. The proposed framework employs a CNN for hierarchical spatial feature extraction and an LSTM to model sequential dependencies among extracted features, followed by federated averaging (FedAvg) to construct the global model from local updates. Experimental evaluation on a private MRI dataset comprising 3,125 scans distributed across three non-IID clients enabled assessment under heterogeneous data distributions. The proposed federated framework achieved an accuracy of 95.20%, compared to 95.95% for centralized training, demonstrating that privacy-preserving decentralized training can achieve competitive diagnostic performance with only a marginal reduction in accuracy. Furthermore, Grad-CAM was employed to provide visual explanations of the model's predictions by highlighting the image regions that contribute to stroke classification. In five-fold cross-validation, the Federated CNN-LSTM model achieved an average accuracy of 92.10% ± 1.50%, indicating consistent performance across folds. The findings demonstrate the feasibility of combining federated learning, hybrid CNN-LSTM representation learning, and model interpretability for privacy-preserving MRI-based stroke classification, while highlighting the need for multi-center validation and larger federated deployments before clinical translation.