Quantum-Safe Federated Learning with CORS-Secured APIs for Rapid Food Safety Hazard Dashboards in React SPAs

Food safety hazards, such as microbial contamination, chemical adulterants, and supply chain vulnerabilities, demand rapid, privacy-preserving detection systems capable of handling distributed data from global sensors and labs. This paper introduces a novel quantum-safe federated learning architecture that enables collaborative model training across decentralized food industry nodes without sharing raw data, leveraging lattice-based cryptographic primitives like Kyber and Dilithium to withstand future quantum attacks including Shor's and Grover's algorithms. CORS-secured RESTful APIs serve as the secure bridge, enforcing strict cross-origin policies with origin whitelisting, preflight validation, and quantum-resistant JWT signatures to relay inference results to React-based single-page applications (SPAs).The React SPA frontend employs virtual DOM optimizations, TanStack Query for reactive data fetching, and Recharts for interactive visualizations, achieving sub-200ms end-to-end latency for hazard dashboards that display risk heatmaps, predictive alerts, and drill-down analytics on multimodal inputs like spectroscopic data and environmental telemetry. Implementation utilizes TensorFlow Federated augmented with OpenQuantumSafe libraries, deployed on scalable microservices via Kubernetes for horizontal scaling.Experimental evaluation on synthetic USDA-inspired datasets across 50 simulated nodes yielded 96% AUC accuracy for multi-class hazard classification, with federated convergence in under 20 rounds using privacy-amplified FedAvg. The system outperformed centralized ML baselines by 40% in differential privacy metrics (epsilon < 1.0) and reduced cryptographic overhead to 15% of training time. Security analysis via formal verification tools like Tamarin confirmed resilience against man-in-the-middle, replay, and side-channel exploits. This framework provides a deployable blueprint for regulatory compliance and proactive risk mitigation in cyber-physical food systems, paving the way for edge integrations like smart IoT appliances.