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Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio

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Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We propose Federated Self-Contextualization (FSC), a multimodal language model framework for in-context clinical audio diagnosis across federated hospital clients.

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