FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Netra Patil; Naveenkumar Jayakumar; Sudan Jha

doi:10.3791/73113

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Method Article

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

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DOI:

10.3791/73113

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September 8th, 2026

Netra Patil¹ , Naveenkumar Jayakumar² , Sudan Jha³

¹Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, ²School of Computer Science and Engineering, Vellore Institute of Technology, ³Department of Computer Science and Engineering, Kathmandu University

Summary

This protocol proposes FedMediFormer-XAI, a multimodal diabetes intelligence framework that respects privacy through federated learning, multimodal transformers, diffusion-based augmentation, graph-based personalized drug recommendation, and explainable artificial intelligence, for accurate prediction, transparent decision-making, and personalized diabetes management.

Abstract

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 healthcare data types, e.g., clinical records, population health indicators, continuous glucose monitoring data, retinal fundus images, wearable sensor measurements, and pharmacological information. To generate synthetic samples and address class imbalance, diffusion models are used, and multimodal transformer architectures are employed to learn relationships among very different data sources. Federated learning enables collaborative training of models in a privacy-preserving manner without sharing raw patient data. The GNN component captures patient-drug and drug-drug interactions for personalized drug recommendations. Explainability methods such as SHapley Additive exPlanations (SHAP), attention visualization, Integrated Gradients, and counterfactual reasoning give transparent interpretations of prediction and recommendation results. In the representative implementation, the proposed framework achieved an accuracy of 94.2%, precision of 93.1%, recall of 92.8%, F1-score of 92.9%, Matthews Correlation Coefficient (MCC) of 0.88, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.96. The graph-based recommendation module achieved precision = 0.89, recall = 0.84, and Normalized Discounted Cumulative Gain (NDCG) = 0.91. The protocol provides a structured approach for integrating heterogeneous healthcare data using multimodal transformers, federated learning, diffusion-based augmentation, graph-based recommendation, and explainable artificial intelligence. The reported computational results demonstrate the framework's potential for diabetes prediction and personalized medication recommendation, while the federated design supports decentralized data handling. A prospective multicenter clinical evaluation is required to establish clinical utility, generalizability, and real-world applicability.

Introduction

Diabetes mellitus is among the top chronic metabolic diseases affecting humanity and poses a major public health issue as it keeps growing, with its long-term effects and the huge costs of treating patients¹^,². According to a report, there are currently over 537 million adults living with diabetes worldwide, and the figure is expected to rise significantly over the next few decades³. If high blood sugar levels are not controlled, the person will suffer from the worst complications, such as coronary heart disease⁴, kidney damage⁵, nerve dam....