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Abstract
The development of smart cities relies heavily on data-driven decision-making to improve urban services, enhance sustainability, and ensure efficient governance. With the rapid growth of IoT devices, sensors, and digital platforms, cities generate massive volumes of heterogeneous and sensitive data across domains such as healthcare, transportation, energy, and public safety. Centralized data analytics, while powerful, poses critical challenges related to privacy, security, bandwidth, and regulatory compliance. Federated Data Analytics (FDA) has emerged as a promising alternative, allowing decentralized model training and collaborative insights without the need to share raw data.
This paper explores the role of FDA in the smart city ecosystem by reviewing its underlying methodologies, including federated learning frameworks, secure aggregation techniques, and privacy-preserving mechanisms such as homomorphic encryption and differential privacy. Key applications are examined in domains like traffic optimization, energy management, healthcare, and environmental monitoring. The study further highlights the technical, organizational, and ethical challenges hindering large-scale adoption, including data heterogeneity, communication overhead, governance issues, and legal constraints. Real-world use cases and pilot projects are analysed to demonstrate practical benefits and limitations.
The findings suggest that FDA can balance innovation with privacy, enabling multi-stakeholder collaboration while safeguarding sensitive data. By integrating FDA with emerging technologies such as blockchain and edge computing, future smart cities can achieve secure, resilient, and citizen-centric urban development.
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References
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