BIG DATA-ENABLED FEDERATED LEARNING FOR SECURE AND COLLABORATIVE INDUSTRIAL IOT IN INDUSTRY

Abstract

The rapid evolution of the Industrial Internet of Things (IoT) has enabled industries to transform conventional manufacturing environments into intelligent, interconnected, and data-driven ecosystems capable of supporting autonomous decision-making and predictive operations. Modern industrial infrastructures continuously generate enormous volumes of heterogeneous data from sensors, controllers, robotic systems, and smart manufacturing equipment. Although centralized machine learning approaches have demonstrated significant potential in predictive maintenance, fault diagnosis, and industrial optimization, they expose sensitive industrial information to privacy risks, cybersecurity threats, communication overhead, and regulatory challenges. To overcome these limitations, this paper proposes a Big Data-Enabled Federated Learning Framework for Secure and Collaborative Industrial IoT that integrates Federated Learning (FL), Big Data Analytics, Machine Learning (ML), Industrial Cybersecurity, Predictive Maintenance, and Distributed Intelligence into a unified architecture. In the proposed framework, industrial organizations collaboratively train global machine learning models without exchanging raw operational data, thereby preserving data ownership and confidentiality. Local model training is performed at individual industrial facilities, while encrypted model parameters are securely aggregated using a federated server. Furthermore, Apache Spark, Hadoop Distributed File System (HDFS), and Apache Kafka are incorporated to support scalable data ingestion, distributed storage, real-time streaming, and high-performance analytics for industrial-scale datasets. Machine learning algorithms are employed to perform predictive maintenance, anomaly detection, fault diagnosis, and operational optimization, whereas cybersecurity monitoring and trust management mechanisms enhance the reliability of collaborative learning. The proposed framework significantly reduces communication overhead, improves scalability, strengthens privacy preservation, and increases resilience against cyber threats compared with conventional centralized architectures. Experimental evaluation demonstrates superior predictive accuracy, lower latency, enhanced resource utilization, and improved industrial decisionmaking capabilities. The proposed framework effectively addresses the growing challenges of Industry 4.0 by enabling secure collaboration among geographically distributed industrial facilities while maintaining operational confidentiality. Consequently, the developed architecture provides a scalable, privacy-preserving, intelligent, and reliable platform for next-generation Industrial IoT applications, facilitating efficient industrial automation, datadriven manufacturing, and sustainable digital transformation.