Privacy-Preserving Edge AI with Graph Transformer for Secure Real-Time Smart Engineering Education in Dense Urban Areas

Engineering education is becoming more advanced in rapidly expanding urban centers with smart connected classrooms, laboratories, wireless networks, indoor environment sensors, and digital learning platform technologies. This establishes three major challenges and requires real-time educational analytics, individual campus data protection, and a robust, deployable system capable of withstanding cyber risks, heavy congestion & heterogeneous edge hardware. However, the majority of existing literature on edge analytics and federated learning or graph-based models, or security monitoring , addresses them individually with limited attention to unifying real-time laurence of secure architectures supporting multi-buildings on engineering campuses. This paper proposes a privacy-preserving edge-graph transformer (PP-EGT) for secure real-time smart engineering education in dense urban environments. The model integrates local anonymization, temporal encoding, dynamic campus graph construction, graph-transformer reasoning, and federated secure aggregation. It jointly predicts a five-class zone state, a continuous Learning Readiness Index (LRI), and a cyber-physical incident probability using multimodal edge features from environmental, network, pedagogical, and security domains. A literature-grounded simulation benchmark representing 48 zones across six campus buildings was constructed over 12 weeks with 5 min sampling. Across five random seeds, PP-EGT achieved 91.89% macro-F1, 96.45% AUROC for incident detection, and 3.43 LRI MAE. Compared with the strongest baseline, ST-Transformer, PP-EGT improved macro-F1 by 1.45 points, reduced p95 latency from 49.59 ms to 44.02 ms, and decreased privacy-leakage AUC from 69.19% to 53.83%. Its novelty lies in jointly optimizing learning analytics, cyber-physical security, privacy, and edge deployment constraints.