## 5 Training Methodology
### Federated Learning Protocol  
To support distributed clinical deployments, TriagE-NLU incorporates a federated learning protocol. Each client (e.g., regional clinic or telemedicine center) trains the model locally on anonymized usage data. Gradients are aggregated securely via federated averaging (FedAvg), with differential privacy mechanisms applied to preserve patient confidentiality. Clients may share partial semantic representations (not raw text) as part of an adaptive knowledge distillation framework. In our proposal, we applied the FedAvg algorithm to enable decentralized training of the TriagE-NLU model across multiple clinical sites. Each participating node (e.g., hospital, telemedicine center) trains its local instance of the model using in-house anonymized data. Instead of transmitting raw patient dialogue or sensitive metadata, only model updates (e.g., gradients or weights) are periodically shared with a central aggregator. The server then performs FedAvg by computing the weighted average of these updates and distributes the global model back to the clients. To strengthen privacy, we incorporated differential privacy techniques and allowed optional exchange of compressed semantic representations, such as EUREKA frames, instead of raw textual data, aligning with an adaptive knowledge distillation approach. This setup ensures data locality, enhances generalizability across institutions, and preserves clinical confidentiality in multilingual direct environments. To maintain data confidentiality in cross-institutional deployments, TriagE-NLU incorporates a layered privacy-preserving design within its federated learning (FL) protocol. Each participating institution, such as a hospital or telemedicine center, trains a local instance of the TriagE-NLU model using only synthetic data stored onsite. At no point are raw patient dialogues or identifiable metadata transmitted externally. Prior to sharing model updates, such as gradient deltas or weight changes, each client applies differential privacy techniques by injecting calibrated noise, which protects model inversion or membership inference attacks that could otherwise compromise input reconstruction. To further ensure the confidentiality of shared information, TriagE-NLU adopts secure aggregation protocols, allowing the central server to compute a global model from encrypted updates without accessing any institution's raw contributions. All communications between client nodes and the central aggregator will be encrypted using standard protocols, such as Transport Layer Security (TLS), to guarantee the integrity and confidentiality of transmitted data. Model updates and semantic representations, when shared, are encrypted both in transit and at rest, using public-key cryptography to ensure that only authorized parties can decrypt aggregate results. Participation in the FL network is controlled through federated identity management and access controls, ensuring that only authenticated endpoints contribute to training and receive model updates. The system is designed to support integration with institutional role-based access mechanisms and to maintain audit logs to ensure traceability and compliance with data governance policies. Instead of exchanging textual inputs, TriagE-NLU supports optional transmission of structured and anonymized semantic frames, such as EUREKA representations, which limit semantic leakage and reinforce data minimization. Although no real clinical data is used in this study, the FL design of TriagE-NLU aligns with data protection frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This comprehensive approach enables the system to operate ethically and securely across diverse, distributed clinical environments. In our experimental simulation, we emulate federated learning across 5-10 clients per round, with local training conducted for one epoch before synchronizing with the central aggregator.