Federated Learning-Based Intrusion Detection in Industrial IoT Networks

Federated learning (FL) is a promising privacy-preserving paradigm for machine learning in distributed environments. Although FL reduces communication overhead, it does not itself provide low-latency guarantees. In IIoT environments, real-time responsiveness is primarily enabled by edge computing and local inference, while FL contributes indirectly by minimizing the need to transmit raw data across the network. This paper explores the use of FL for intrusion detection in IIoT networks and compares its performance with traditional centralized machine learning approaches. A simulated IIoT environment was developed in which each node locally trains a model on synthetic normal and attack traffic data, sharing only model parameters with a central server. The Flower framework was employed to coordinate training and model aggregation across multiple clients without exposing raw data. Experimental results show that FL achieves detection accuracy comparable to centralized models while significantly reducing privacy risks and network transmission overhead. These results demonstrate the feasibility of FL as a secure and scalable solution for IIoT intrusion detection. Future work will validate the approach on real-world datasets and heterogeneous edge devices to further assess its robustness and effectiveness.