Enhancing Smart Cities With Federated Learning: A Framework for Secure, Scalable, and Intelligent Urban Sensing Systems

With the advancement of technology, devices have become increasingly smart by leveraging IoT, mobile sensors, and edge computing. However, traditional centralized data processing methods raise concerns regarding privacy, scalability, and network inefficiency in smart city applications. Federated learning (FL) has emerged as a promising alternative for urban sensing systems, enabling decentralized data processing while maintaining user privacy. Despite its potential, challenges such as data heterogeneity, resource constraints, and privacy preservation remain for urban sensing systems. In this article, we discuss the architecture of FL for urban sensing, key enabling technologies, and real-world applications and explore future directions to overcome these challenges for smart cities.