Perspective paper outlines multimodal federated foundation models for biomedical data, highlighting their potential for privacy-preserving distributed learning.
In this perspective paper, we introduce multimodal multi-task federated foundation models (M3T FedFMs) as a paradigm for privacy-preserving and distributed learning over biomedical sensing and imaging data. We outline their architecture, applications across medical sectors, key challenges, and future research directions, as well as the metrics and datasets that can facilitate their benchmarking.
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Chakrabarty et al. (2026) studied this question.
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