Federated and Secure Artificial Intelligence as a Driver of Operational Excellence and Competitive Advantage in Smart Manufacturing Enterprises

The rapid advancement of Industry 4.0 and adoption of cyber-physical production systems (CPPS) demand real-time, adaptive, and privacy-preserving optimization that conventional centralized AI architectures cannot adequately provide. Such approaches remain susceptible to data privacy vulnerabilities, regulatory non-compliance (GDPR, CCPA), communication latency, and insufficient responsiveness to dynamic production variability within edge device constraints. Although federated learning (FL) offers a promising paradigm for distributed privacy-sensitive intelligence, existing implementations fail to address practical security requirements and hardware limitations of shop-floor edge environments, rendering real-world deployment infeasible. This study introduces FedSecure-OPE, a secure autonomous AI framework designed to concurrently optimize production scheduling, quality management, and predictive maintenance across distributed manufacturing cells. The framework integrates homomorphic encryption-based federated aggregation, secure multi-party computation (SMPC) for model updates, and dynamic neural architecture search subject to edge hardware constraints. FedSecure-OPE is evaluated against centralized deep learning (Model A) and unsecured federated learning (Model B) using the Manufacturing Cyber-physical Middleware Testbed (MCMT) and Synthetic Manufacturing Trace (SMT) datasets. All experimental results were obtained through digital twin simulation under hardware emulation and have not been validated on physical edge environments. Within this context, FedSecure-OPE (Model C) achieves 31.2% and 16.8% operational performance improvements over Models A and B respectively, reduces edge energy consumption by 43.7%, attains 99.2% cryptographically protected model-update coverage under defined simulation security assumptions, and maintains average inference latency of 38 ms per control cycle. These findings establish a simulation-based foundation for security-conscious federated AI in smart manufacturing, while underscoring the necessity of future validation in physical environments.