Title:Hybrid CNN–Transformer Architecture with Federated Learning for Privacy-Preserving Smart City Applications: A Comprehensive Analysis of Global Implementations and Future Directions Summary:This research paper introduces a novel hybrid deep learning framework that combines Convolutional Neural Networks (CNNs) and Transformers, deployed using federated learning, to address privacy and scalability challenges in smart city applications. The approach enables local data processing on edge devices, ensuring sensitive information never leaves its source, and applies differential privacy techniques to provide formal privacy guarantees. Key Contributions: Hybrid Model: Integrates CNNs for local feature extraction and Transformers for global context, optimized for distributed, privacy-preserving learning. Federated Learning: Allows collaborative model training across multiple devices without centralizing raw data, supporting compliance with privacy regulations like GDPR and CCPA. Differential Privacy: Implements adaptive noise mechanisms to balance privacy and model accuracy. Comprehensive Evaluation: Demonstrates strong performance (93.5% mean accuracy, 15.2% improvement over baselines) across five real-world smart city datasets, including traffic, air quality, and energy management. Market and Case Study Analysis: Reviews global smart city investment trends and presents case studies from Barcelona, Singapore, and Toronto, highlighting different governance and privacy models. Significance:The paper provides a scalable, privacy-preserving AI solution for smart cities, addressing regulatory, technical, and practical deployment challenges. It offers both theoretical innovation and real-world validation, making it relevant for researchers, practitioners, and policymakers in urban computing and AI.