Federated Learning for Ransomware-Resilient Industrial IoT: A Decentralized Framework for Secure AI at the Manufacturing Edge

Abstract Industrial Internet of Things (IIoT) deployments are facing increasing cybersecurity threats, especially with ransomware attacks on operational technology infrastructure. Traditional centralized machine learning configurations with the storage of manufacturing data in a single repository expand the attack surface area. Federated learning presents a completely new approach to conducting distributed model training across manufacturing sites with data locality. The federated learning framework uses secure aggregation protocols and encrypted communication channels to deliver intelligent systems without sending raw operational data externally. The federated model decreases the threat of ransomware propagation and exfiltration of operational data by establishing strong access control measures at the edge nodes and employing homomorphic encryption techniques. The federated approach is particularly useful in multi-site manufacturing use cases where regulatory compliance and maintaining intellectual property remain primary concerns. Demonstrations and deployments of the proposed framework in actual research problems spanning predictive maintenance, quality control, and process optimization show the model can maintain model accuracy while enhancing the operational resilience of IIoT applications. The intersection of distributed intelligence principles and cybersecurity principles provides a pathway for trustworthy AI systems in critical industrial infrastructures.