AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inter-stethoscope variability. We introduce a federated domain generalization (FedDG) formulation for RSC under stethoscope-induced device shifts, where clients use heterogeneous devices and the model is evaluated on unseen devices. Our empirical analysis shows that stethoscope-induced style and disease-specific content are tightly entangled, making deterministic style removal unreliable. In response, we propose a causality-inspired multimodal FedDG framework that combines: (i) a causality-inspired device style intervention network that performs content-preserving style perturbations, (ii) counterfactual text augmentation that neutralizes metadata shortcuts, and (iii) gradient alignment that facilitates device-invariant representations across clients. Built on a multimodal language-audio pretraining model, it outperforms conventional data augmentation and federated learning baselines in leave-one-device-out validation on ICBHI and SPRSound datasets. Code will be released upon publication.

翻译：AI驱动的呼吸音分类（RSC）在自动化肺部疾病检测中具有广阔前景，但多站点部署受限于听诊器间的变异性。针对听诊器导致的设备偏移问题，我们提出了一种联邦域泛化（FedDG）框架，其中客户端使用异构设备，模型需在未见过的设备上进行评估。实证分析表明，听诊器引发的风格特征与疾病特异性内容紧密耦合，使得确定性风格去除方法不可靠。为此，我们提出了一种因果启发的多模态FedDG框架，其结合了：(i) 基于因果启发的设备风格干预网络，可执行保持内容的风格扰动；(ii) 反事实文本增强方法，用于消除元数据快捷方式；(iii) 梯度对齐方法，促进各客户端间设备不变表征的构建。基于多模态语言-音频预训练模型，该方法在ICBHI和SPRSound数据集上的留一设备验证中优于传统数据增强和联邦学习基线。相关代码将于发表后公开。