Quantum-Resilient Federated Learning for Secure and Scalable Cyber-Physical Systems

Cyber-Physical Systems like smart grids, autonomous cars, and industrial IoT widely implement Federated Learning (FL) to provide distributed intelligence with privacy-protected data. Yet, the impending quantum threat makes conventional cryptographic methods in FL pipelines obsolete, exposing critical infrastructure to future security vulnerabilities. This paper presents Quantum-Resilient Federated Learning (QR-FL), a new framework integrating lattice-based post-quantum cryptography, light-weight zero-knowledge proofs, and trust-aware aggregation ensuring confidentiality, integrity, and quantum/classical attack resistance. Through comprehensive experimentation on real-world CPS datasets, QR-FL provides up to 48% enhanced adversarial robustness, 32% communication overhead savings, and 6.7% enhanced model accuracy compared to current state-of-the-art secure FL solutions. By achieving future-proof security with scalable federated intelligence, QR-FL provides an architecture foundation for future CPS, offering a landmark direction for secure, decentralized AI in the quantum age.