Towards Quantum-Resilient Food Systems: Federated AI and Lightweight Lattice Hashing for Blockchain-Based Traceability

Towards Quantum-Resilient Food Systems: Federated AI and Lightweight Lattice Hashing for Blockchain-Based Traceability

연구 분야: Verification

학회: SN Computer Science

Food fraud, contamination, and infrastructure issues greatly weaken the security and clarity of worldwide food supply chains. This research presents a comprehensive framework that integrates Federated Learning (FL), Quantum-Inspired Deep Reinforcement Learning (QI-DRL), and Lightweight Lattice-Based Cryptographic Hashing (LBC-H) within a blockchain context. The suggested framework facilitates decentralised, privacy-focused AI training among supply chain participants, enhances blockchain efficiency for instant anomaly detection with 99.2% accuracy and under 50 ms latency, and safeguards transactions through quantum-resistant, energy-efficient cryptography ideal for IoT devices. This integration guarantees high throughput (1500 TPS), enhanced scalability, lower power usage, and more than 99% protection against cyber threats–including quantum attacks. Integrating FL, QI-DRL, and LBC-H establishes a novel standard for secure, transparent, and quantum-resistant monitoring of the food supply chain.