Network Slicing for Federated Learning in Operational Technology Environment

Conference · Mon May 26 00:00:00 EDT 2025

DOI:https://doi.org/10.1109/ICMLCN64995.2025.11140448· OSTI ID:3003235

Delgado, Brian G. Rodiles ^[1]; Aguirre, Luis D. Estrada ^[1]; Marfo, William ^[1]; Servin, Christian ^[2]; Tosh, Deepak K. ^[1]

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1. University of Texas at El Paso,Department of Computer Science,TX,USA
2. El Paso Community College,Department of Computer Science,TX,USA

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

• Research Organization: National Energy Technology Laboratory
• Sponsoring Organization: Office of Fossil Energy; Department of Energy
• DOE Contract Number: FE0032089
• OSTI ID: 3003235
• Country of Publication: United States
• Language: English