Decentralized Intelligence for Smart Cities: Integrating Federated Learning, Blockchain, and Foundation Models for Privacy-Preserving Urban Data Management
Shruti Hardia · 2025
The growing complexity of urban data management in smart cities necessitates innovative solutions that balance predictive accuracy, scalability, and privacy. This paper proposes a novel framework that integrates Federated Learning (FL), Blockchain, and Foundation Models (FMs) to address these challenges. By leveraging FL, data is processed locally on edge devices, ensuring that sensitive information remains private and secure, while the predictive power of FMs enhances real-time decision-making across urban systems. Blockchain technology ensures transparency and trust, providing an immutable ledger for secure governance and operations. The proposed system enables seamless collaboration across diverse data sources while preserving privacy and fostering a decentralized, resilient infrastructure. A practical use case in inventory management and sales forecasting for businesses within a smart city is presented, showcasing how this framework can enhance operational efficiency, reduce costs, and maintain high standards of data protection. This integrated approach sets a new standard for secure, adaptive, and efficient smart city management.