Hier-FedMeta: A Hierarchical Federated Meta-Learning Framework for Personalized and Efficient IoV Systems

The Internet of Vehicles (IoV) enhances smart city functionalities by interconnecting diverse components, yet it introduces significant challenges in terms of user privacy, communication efficiency, and energy consumption. Traditional federated learning frameworks, while adept at addressing these concerns, fall short in personalization due to heterogeneous data distributions among clients. To overcome this, we introduce Hier-FedMeta, a novel framework that combines hierarchical federated learning with meta-learning to provide tailored and efficient solutions. Our comparative analyses with four estab-lished methods show Hier-FedMeta's superior generalization capabilities and adaptability, achieving enhanced performance with minimal computational overhead after just one update step. Furthermore, our in-depth analysis of aggregation parameters offers valuable insights for the optimization of hierarchical federated meta-learning architectures, representing a significant step forward in personalized learning for IoV in smart cities.