Federated Data Engineering Revolutionizing Privacy-Preserving Approaches to Distributed Data Integration

The growing necessity for secure and privacy- preserving data integration is crucial as organizations face increasing concerns about data breaches, regulatory compliance, and ethical data usage. Traditional centralized approaches expose sensitive data to potential vulnerabilities, making it imperative to develop decentralized solutions such as federated learning (FL) that balance collaboration and data privacy, especially in high-stakes domains like traffic management, healthcare, and finance. This study introduces a Privacy-Preserving Federated Learning-Traffic State Estimation (PPFL-TSE) framework, enabling multiple parties to collaboratively train machine learning models without exposing sensitive raw data. The study leverages a vertical federated learning architecture, integrating datasets from mobility providers and municipal authorities to predict traffic density and flow, ensuring improved accuracy and privacy preservation. The framework incorporates decentralized data processing with advanced privacy-preserving techniques, including differential privacy and gradient encryption, effectively mitigating risks related to data breaches and adversarial attacks. Benchmarking against traditional and privacy-preserving models, PPFL-TSE consistently demonstrates superior prediction accuracy and computational efficiency across varying penetration rates. Performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) validate the model's effectiveness in traffic density and flow predictions under diverse data penetration scenarios. Notably, PPFL-TSE-cell (Q=1) achieves optimal performance with a density RMSE of 2.65 and flow MAE of 0.59 at an 80% penetration rate, outperforming all other models. The study showcases the scalability, robustness, and efficiency of the proposed framework for intelligent traffic management, demonstrating that privacy preservation can be achieved without compromising performance.