Yuping Yan, Yaochu Jin · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27644 · open-access PDF
La optimización federada sobre datos multimodales heterogéneos permite a las instituciones distribuidas optimizar de forma colaborativa un modelo compartido utilizando imágenes médicas, perfiles genómicos, registros clínicos y señales fisiológicas sin centralizar datos primos. Sin embargo, la protección de los datos específicos de la modalidad a lo largo del ciclo de vida de la optimización sigue siendo insuficientemente explorada, especialmente cuando las modalidades heterogéneas requieren claves de cifrado separadas y acceso local selectivo.
Machine translation (Meta M2M100); the English original follows.
Federated optimization over heterogeneous multimodal data enables distributed institutions to collaboratively optimize a shared model using medical images, genomic profiles, clinical records, and physiological signals without centralizing raw data. However, protecting modality-specific data throughout the optimization lifecycle remains insufficiently explored, particularly when heterogeneous modalities require separate encryption keys and selective local access. This paper proposes a secure multimodal federated optimization framework that separates local data protection from federated-update p
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