Emergency triage artificial intelligence (AI) is usually evaluated as a centralized prediction problem, yet many hospitals cannot pool patient-level records across governance boundaries. We studied a narrower and safety-relevant task: within-dataset federated simulation for detecting Emergency Severity Index (ESI)-2/ESI-1 encounters using information available before or during triage. Zendal is a reproducible within-dataset federated evaluation framework for critical emergency triage escalation. It defines a triage-time feature boundary, site-aligned cross-silo splits, training-split-only imputation and scaling, validation-selected operating thresholds, bootstrap uncertainty, decision-curve analysis, calibration analysis, local-only baselines, per-site and subgroup diagnostics, and leave-one-site-out transfer stress testing. In 558,029 public emergency department encounters, the endpoint was binary critical-band ESI assignment (ESI-2/ESI-1 vs. ESI-3/ESI-4/ESI-5), not an independently adjudicated clinical outcome. The feature set contained 240 pre-triage variables: demographics, arrival metadata, previous utilization counts, triage vital signs, and chief-complaint indicators; disposition, diagnoses, medications, procedures, imaging, laboratories, and target-derived imputations were excluded. A centralized logistic reference reached AUROC 0.879 (95% CI: 0.876–0.882), average precision 0.767, sensitivity 0.900 (95% CI: 0.895–0.905), specificity 0.654 (95% CI: 0.649–0.658), and positive predictive value (PPV) 0.530 at a validation-selected threshold targeting at least 90% sensitivity. Federated logistic training across three site-aligned clients nearly matched the centralized reference: AUROC 0.878 (95% CI: 0.875–0.880), average precision 0.767, sensitivity 0.900 (95% CI: 0.896–0.904), specificity 0.650 (95% CI: 0.645–0.655), and PPV 0.527. Post hoc Platt scaling reduced the federated expected calibration error from 0.160 to 0.011, supporting recalibration before direct risk interpretation. Model-guided escalation produced a positive mean decision-curve net benefit across thresholds 0.05–0.50 (0.164 for federated logistic vs. −0.003 for flag-all and 0 for flag-none). In leave-one-site-out transfer tests, federated source-site models retained AUROC 0.846–0.876 and sensitivity 0.871–0.909 on held-out sites, but the low-prevalence site had PPV 0.107 and negative net benefit. An open MIMIC-IV-ED Demo smoke test supported schema feasibility but was too small for external validation. Zendal is therefore a transparent framework for no-raw-row-exchange triage model evaluation, not a deployment-ready clinical system or a demonstration of formal privacy protection.