Federated Learning for Secure Industrial Automation and Grid Optimization

The rapid digital transformation of industrial automation and smart power grids has led to large scale deployment of data driven intelligence across distributed cyber physical systems. While centralized machine learning techniques have demonstrated effectiveness in predictive maintenance, fault detection, and energy optimization, they introduce critical concerns related to data privacy, cybersecurity, and regulatory compliance. Sensitive operational data generated by industrial equipment and grid infrastructure cannot be freely shared across organizational or geographic boundaries. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training without centralized data aggregation. This paper investigates the application of federated learning for secure industrial automation and grid optimization. A federated framework is proposed in which edge devices and local control centers collaboratively train global models while preserving data confidentiality. The methodology integrates secure aggregation, communication efficient model updates, and adaptive learning strategies suitable for heterogeneous industrial environments. Performance evaluation demonstrates that the proposed FL based approach achieves accuracy comparable to centralized learning while significantly enhancing privacy protection and system resilience. The results indicate that federated learning is a viable and scalable solution for next-generation industrial automation and smart grid optimization.