###### Abstract
In health contexts, FL has been applied to medical imaging [17] and predictive modeling [18]. However, NLP for emergency settings presents additional challenges in terms of synchronization, adaptation, and the trustworthiness of models across various telemedicine deployments. Cross-lingual transfer and multilingual models have been explored for general NLP tasks. Nevertheless, emergency care introduces linguistic variation, code-switching, and cultural differences in symptom reporting. There is a pressing need for models that can generalize under such conditions and perform semantic understanding even when resources or parallel data are limited. Recent advancements in multilingual clinical NLP include systems such as mT5 [19], mBART [20], and XLM-RoBERTa [21], which have enabled cross-lingual modeling of healthcare text. However, these systems remain limited in their ability to jointly model both semantic understanding and intervention planning, especially in low-resource or multilingual clinical settings. For example, GatorTron [22] offers high performance in English-language EHRs but is not designed for dialogue-based input. Other tools, such as MedDG [23], focus on generative dialogue but lack structured intervention classification. To date, no comprehensive system integrates multilingual dialogue parsing, patient context, and clinical action mapping within a privacy-aware framework. We hypothesize that a multilingual NLP system trained on structured emergency input data can accurately map clinical utterances to intervention decisions across languages and resource settings. To address these challenges, this paper introduces TriagE-NLU, a semantic understanding system tailored for emergency telemedicine triage and intervention support. The name TriagE-NLU comes from combining two key concepts central to the paper: _TriagE_ refers to medical triage, the clinical process of prioritizing patients based on the urgency of their conditions. This reflects the system's purpose: to understand medical dialogues and support urgent assessment and intervention decisions in emergency telemedicine. On the other hand, _NLU_ stands for natural language understanding, a subfield of natural language processing (NLP) that focuses on machine understanding of human language. Put together, TriagE-NLU designates a natural language understanding system for clinical triage capable of interpreting multilingual medical dialogues, assessing urgency, and recommending interventions based on a structured semantic framework. TriagE-NLU enables structured understanding of multilingual conversations between patients and remote clinicians. It fuses natural language with patient metadata (e.g., age, known conditions, symptom onset), contextual cues (e.g., temporal markers, urgency expressions), and ontology-grounded semantics to recommend appropriate clinical interventions. Built on a federated learning framework, the system continuously improves through privacy-preserving updates across institutions. This paper makes the following key contributions:  
* We develop TriagE-NLU, a multilingual, patient-aware natural language understanding system that combines semantic parsing and intervention classification for emergency telemedicine. * We introduce EUREKA (Emergency Understanding Representation for Evidence-based Knowledge and Action). This unified semantic schema encodes symptoms, onset patterns, risk factors, and inferred urgency to map patient dialogue to structured intent. * We construct TriageX, the first synthetic, clinically annotated dataset for multilingual emergency dialogue understanding, and train our model using federated averaging (FedAvg) to support privacy-preserving, cross-institutional deployment. Together, these components establish a new foundation for NLP in emergency medicine, shifting from retrospective analysis to dynamic, immediate clinical reasoning that supports equitable and timely care.