Federated Learning (FL) is decentralized machine learning, which preserves data confidentiality and security but suffers challenges such as high communication overhead, latency, and scalability issues in large scale smart city networks. We propose a Hierarchical Federated Learning (HFL) framework which takes advantage of fog nodes to address these problems. HFL framework ensures that the cost of communication is cut back through the introduction of the multilevel aggregation strategy where local models are aggregated first at fog nodes before they are combined at a central server. Communication costs are reduced while latency is improved and scalability is enhanced by this HFL framework through simulations on real-world smart city datasets. On edge devices, our simulation results with real-world datasets from smart cities show that compared to traditional FL it reduces communication overhead by up to 50%, achieves faster model convergence with similar accuracy, and leads to lower energy consumption. This framework represents a big step forward towards deployment of FL in smart cities making it efficient and scalable in resource-constrained environments.