Federated learning-enabled inverse design of photonic crystals with blockchain-secured collaboration for next-generation AI hardware

This paper introduces a novel decentralized framework that integrates federated learning with blockchain technology to enable secure and privacy-preserving inverse design of photonic crystals for next-generation AI hardware. Our approach enables distributed collaborators to jointly optimize photonic crystal structures through local neural network training on proprietary simulation data, removing the need for central data sharing. A federated neural architecture search method automates the discovery of optimized network architectures tailored for photonic design tasks, improving prediction accuracy and accelerating convergence. The blockchain layer ensures tamper-proof, verifiable coordination of model updates through a permissioned ledger and smart contracts, enhancing trust and accountability among participants. Experimental evaluations demonstrate that the combined federated NAS and blockchain framework achieves superior design accuracy, efficient training, robust collaboration security, and minimal communication overhead. This integrated solution provides a scalable and trustworthy foundation for collaborative AI-driven photonic hardware design.