aceFedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes
Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized therapeutic guidance. This research work introduces FedMediFormer-XAI, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks (GNNs) for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence. The system utilizes diverse healthcar…
FedMediFormer-XAI combined federated learning, multimodal transformers, diffusion augmentation and GNN drug recommendation to achieve 94.2% accuracy for diabetes prediction and NDCG 0.91 for personalized drug recommendation while enabling privacy-preserving training without sharing raw patient data.
Framework has only been tested in a representative implementation; clinical utility, generalizability and real-world applicability remain unproven pending prospective multicenter evaluation.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-09-08
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Truvace Impact Record TRV-2026-1052, v1: “FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes.” Truvace, 2026-09-10. /record/TRV-2026-1052 (accessed at citation time). sha256 cc9aee932af14a75…
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