Secure Telemedicine in Medical Tourism: A Federated Learning Architecture for Privacy-Preserving Cross-Border Healthcare

The rapid expansion of medical tourism and international travel has necessitated robust cross-border telemedicine networks. However, sharing electronic health records (EHRs) across international borders introduces severe privacy, legal, and security challenges due to conflicting regulatory frameworks such as the GDPR and regional data protection laws. This paper proposes a novel Privacy-Preserving Federated Learning (PPFL) architecture specifically designed for cross-border medical tourism networks. By utilizing federated learning, our framework enables international hospital networks to collaboratively train predictive healthcare models without transferring raw patient data across jurisdictions. We introduce a simulated Cross-Border Electronic Health Record (CB-EHR) dataset comprising 50,000 synthetic medical tourist profiles distributed across five international nodes. Our methodology integrates localized model training with homomorphically encrypted secure aggregation. The experimental results demonstrate that our federated approach achieves a predictive accuracy of 92.4% in patient readmission risk classification, performing within 1.1% of highly restricted centralized models, while reducing data leakage probability to near zero. This framework provides a scalable, compliant, and secure technological foundation for the future of travel medicine and cross-border healthcare integration.