Secure and Decentralized AI for Smart Cities: A Federated Learning and Blockchain–Enabled Predictive Analytics Framework

The rapid urbanization of modern cities has necessitated the adoption of intelligent systems to optimize resource allocation, enhance public services, and improve overall urban living standards. Artificial Intelligence (AI)-driven predictive analytics has emerged as a pivotal tool in smart city development, enabling data-driven decision-making for traffic management, energy distribution, healthcare, and public safety. However, traditional centralized AI models face significant challenges, including data privacy risks, security vulnerabilities, and scalability limitations. This paper introduces an AI-Powered Predictive Analytics Framework (AIPAF) that integrates Federated Learning (FL) and Blockchain Security to overcome these challenges. Federated Learning enables collaborative model training across distributed edge devices without requiring raw data exchange, thereby preserving privacy and reducing communication overhead. Blockchain technology ensures data integrity, transparency, and tamper-proof model updates through decentralized consensus mechanisms. Experimental evaluations demonstrate that AIPAF achieves a 32% improvement in predictive accuracy, a 40% reduction in data transmission costs, and a 28% enhancement in privacy compliance compared to conventional cloud-based AI systems. Additionally, blockchain integration reduces unauthorized data access incidents by 45%, establishing a secure and auditable environment for AI-driven urban analytics. The framework’s scalability is validated across diverse smart city applications, including traffic optimization, energy efficiency, and environmental monitoring. This study underscores the transformative potential of combining Federated Learning and Blockchain to create a secure, privacy-preserving, and scalable AI infrastructure for smart cities. The findings highlight the framework’s ability to address critical challenges in urban AI deployments while ensuring regulatory compliance and operational efficiency.