Kinshi Sinha

This paper presents a comparative study of centralized machine learning and federated learning for intrusion detection in Industrial Internet of Things (IIoT) gateway networks using the X-IIoTID dataset. Centralized models achieve high detection accuracy but rely on data aggregation, raising privacy and scalability concerns. To address this, a federated learning framework based on the FedAvg algorithm is implemented with distributed clients.