Privacy Preserving Federated Learning Frameworks for Secure, Scalable, and Citizen-Centric AI in Smart City Ecosystems

Smart cities increasingly depend on data-driven intelligence to enhance healthcare, mobility, energy, and citizen services, yet centralized AI models raise concerns over privacy, security, and regulatory compliance. Federated Learning (FL) emerges as a privacy-preserving paradigm that trains models locally on IoT devices, smart meters, and mobile systems while sharing only model updates. This chapter explores FL's architecture, core components, and integration with enabling technologies such as Blockchain, Differential Privacy, and Edge AI. Real-world use cases, including Traffic Management, Healthcare Diagnostics, and Energy Optimization, demonstrate its transformative potential. Challenges like device heterogeneity, communication overhead, and data imbalance are critically analyzed, with solutions and future policy directions proposed to ensure ethical, secure, and sustainable adoption of FL in citizen-centric smart cities.

Hali tarjima qilinmagan