Federated Multi-Modal Learning System for Privacy-Preserving Medical Diagnostics in Next-Generation Healthcare

The high-growth rate of multi-modal clinical data has necessitated the need to develop diagnostic models that are both patient confidential and provide high predictive accuracy. The proposed work presents a Federated Multi-Modal Learning System that incorporates imaging, clinical text, and structured EHR capabilities through decentralized optimization to avoid sharing raw data. The system uses secure aggregation, multi-branch encoders, and an attention-based fusion unit, which is trained using privacy-sensitive gradient updates. Experimental analysis shows significant improvements in the performance of 97.4% diagnostic accuracy, 96.1% F1-score, 0.982 AUC, and 28% of latency reduction over local uni-modal baselines. The model achieves a 35% enhancement in multi-modal features, a 42% reduction in communication cost, and 0% risk of data exposure, making it feasible in a heterogeneous clinical node. The findings verify that federated multi-modal integration increases diagnostic reliability and ensures a high level of confidentiality. The findings indicate that the proposed system will be a scalable backbone to the next-generation privacy-preserving medical diagnostics.