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Experimental Validation of a Federated Explainable Multi-Modal Transformer–GAN Framework with AutoML-Optimized Ensemble Learning for Bias-Resilient, Privacy-Preserving and Resource-Efficient Real-Time Disease Prediction Across Heterogeneous Multi-Site Populations

2026-05-05

Abstract excerpt

<title>Abstract</title> <p>In recent years, the demand for real-time disease prediction has increased significantly, especially in environments where patient data are distributed across multiple healthcare institutions. Traditional centralized learning approaches often suffer from issues such as data bias, lack of transparency, inconsistent performance, and high computational requirements. These challenges reduce...

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Literature Corpus work: fa14e1d9-5fec-5fcb-874c-58217bd5f82e
DOI: 10.21203/rs.3.rs-9590548/v1

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Experimental Validation of a Federated Explainable Multi-Modal Transformer–GAN Framework with AutoML-Optimized Ensemble Learning for Bias-Resilient, Privacy-Preserving and Resource-Efficient Real-Time Disease Prediction Across Heterogeneous Multi-Site PopulationsDOI 10.21203/rs.3.rs-9590548/v1Read

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