FEDXAI-SPLITMED: FEDERATED SPLIT LEARNING WITH DIFFERENTIAL PRIVACY AND EXPLAINABLE AI FOR MEDICAL VISUAL QUESTION ANSWERING

Medical Visual Question Answering (Med-VQA) faces persistent challenges in protecting patient privacy, explaining model decisions, and reducing communication costs in distributed hospital networks. This paper presents FedXAI-SplitMed, an end-to-end framework that jointly addresses these challenges using Federated Split Learning with a ViT-B/16 encoder split at layer6, client-side MedCGAN augmentation, MPI ring-allreduce gradient compression, and a tri-modal XAI module integrating Grad-CAM++, SHAP DeepExplainer, and cross-modal attention rollout. Only differentially private smashed activations ($\epsilon =1.2$, $\delta =10^{-5}$) are transmitted, while upper layer gradients are sparsified using Top-$K=0.1$. On VQA-RAD, FedXAI-SplitMed achieves 91.4% closed-ended and 74.8% open-ended accuracy, while reducing communication by 96% (2,100MB to 78 MB) and client computation by 64% compared with vanilla FedAvg. BioSentBERT based evaluation yields 93.1% effective clinical accuracy. A hierarchical aggregation mechanism further consolidates client encoders, with its numerical stability validated independently over 400 synthetic training rounds. The results demonstrate that FedXAI-SplitMed enables privacy preserving, interpretable, and communication efficient distributed Med-VQA.