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

• Authors
• Authors and affiliations

ABSTRACT

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.

Significance of the Study:

This study introduces a secure and decentralized AI framework (AIPAF) for smart cities, combining Federated Learning (FL) and Blockchain to enhance privacy, security, and scalability in predictive analytics. By enabling localized model training and tamper-proof data integrity, AIPAF improves predictive accuracy (32%), reduces data transmission costs (40%), and strengthens privacy compliance (28%). The framework addresses critical challenges in urban AI, offering a resilient solution for traffic, energy, and public safety management while ensuring regulatory adherence and citizen-centric smart city development.

Summary of the Study:

The study proposes AIPAF, a Federated Learning and Blockchain-based framework for secure AI-driven smart city analytics. AIPAF decentralizes model training, preserving privacy and reducing data costs by 40%, while blockchain ensures tamper-proof security, cutting unauthorized access by 45%. It achieves 32% higher accuracy than centralized AI and improves energy efficiency by 25%. Challenges like computational overhead remain, but AIPAF sets a foundation for future research in quantum-resistant encryption and explainable AI, advancing scalable, privacy-aware urban intelligence.