Federated Learning-Based Multi-Task Framework for Privacy-Preserving Diagnosis of Muscle Invasion in Bladder Cancer.

Accurate diagnosis of muscle-invasive bladder cancer (MIBC) is critical for effective treatment planning, yet remains challenging owing to the scarcity of large-scale multicenter datasets and stringent data privacy requirements. To address these issues, we propose a federated learning (FL)-based framework for privacy-preserving MIBC diagnosis. The framework enables multi-institutional collaboration by training local models on site-specific data and aggregating them into a global model through secure parameter exchange, without transferring raw data. Furthermore, we introduce a multi-task learning architecture that simultaneously performs tumor segmentation and invasion classification, supported by a split-attention convolutional module designed to improve feature discrimination. Experimental results on MRI datasets from four medical centers show that our proposed multi‑task federated learning framework yields an AUC of 0.844, outperforming both single‑center models and the single‑task federated baseline (AUC = 0.829), and exhibits robustness under heterogeneous data distributions. Ablation experiments showed that incorporating the split-attention convolutional module increased the AUC of the multi-task FL model from 0.818 to 0.844.