Federated Learning Framework for Privacy-Preserving Environmental Compliance Monitoring Across Industrial Sites

Abstract

The growing adoption of digital monitoring systems in industrial operations has increased the demand for secure and privacy-preserving environmental compliance management solutions. This study presents a federated learning framework for privacy-preserving environmental compliance monitoring across distributed industrial sites. The proposed framework enables collaborative machine learning model development without requiring direct sharing of sensitive operational or environmental data between industries and centralized monitoring systems. The architecture integrates distributed environmental sensing, local data processing, and federated model aggregation to support real-time compliance assessment while maintaining data confidentiality and cybersecurity integrity. Key environmental parameters, including air emissions, wastewater discharge quality, energy consumption, and pollutant concentration levels, were continuously monitored and analyzed using decentralized learning mechanisms. The framework employs secure model update transmission and adaptive optimization algorithms to improve prediction accuracy and anomaly detection across heterogeneous industrial environments. Performance evaluation demonstrated that the federated learning approach achieved high monitoring reliability and predictive capability comparable to centralized machine learning systems while significantly reducing risks associated with data exposure and unauthorized access. Furthermore, the decentralized architecture enhanced scalability, operational flexibility, and regulatory transparency across multiple industrial facilities. Comparative analysis indicated improved compliance monitoring efficiency, reduced communication overhead, and enhanced resilience against data breaches in distributed industrial networks.