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

arXiv: https://arxiv.org/abs/2606.23243 · HTML: https://mlsanity.com/paper/2606.23243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 19 of 19)

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- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
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- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
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- [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025)
- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [HeAR -- Health Acoustic Representations](https://mlsanity.com/paper/2403.02522/) (2024)
- [Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1](https://mlsanity.com/paper/2102.03049/) (2021)
- [CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning](https://mlsanity.com/paper/2505.01199/) (2025)

## Abstract (arXiv)

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. FSC constructs pseudo-label episodes via unsupervised clustering of audio representations, bypassing scarce real diagnostic labels, and enables contextual reasoning from support-query pairs. Our progressive three-stage pipeline first aligns audio embeddings with the language model via caption-based pretraining, then adapts it for episodic in-context inference through federated optimization. At test time, given a small labeled support set, the model diagnoses an unseen query through multimodal reasoning. On held-out respiratory and cardiac conditions, FSC achieves 71.6% accuracy in 2-way 2-shot evaluation, outperforming audio-language baselines by over 9%.