Federated Learning in Smart Cities

Smart cities are integral to meeting modern societal demands across industrial, medical, economic, and educational sectors, where secure communication among devices is pivotal for optimal performance. Federated Learning (FL) emerges as a transformative technique addressing data accessibility challenges within Internet of Things (IoT) and metaverse-driven smart cities. By establishing a federative model of local updates and model aggregation, FL enhances operational efficiencies crucial for industrial processes, healthcare systems, drug discovery, medicine recommendations, and fault tolerance mechanisms. This chapter elucidates FL's roles and architectures in augmenting or diminishing smart city performances across these domains. Novel techniques such as AI, Q-learning, biologically inspired computing, and evolutionary algorithms are explored for their potential in bolstering FL's effectiveness within smart city frameworks. Mathematical formulations detailing node, task, and graph mappings, alongside fault tolerance models, are presented to evaluate these approaches' efficacy. This research critically assesses FL's impact on diverse smart city applications, underscoring its practical implications in real-world device settings.