HDFL: A Hierarchical Decentralized Federated Learning Framework for Dynamic and Heterogeneous IoV Environments

Traditional federated learning (FL) approaches face significant challenges when applied to dynamic and heterogeneous Internet of Vehicles (IoV) environments, which are characterized by frequent node mobility, unstable communication<br/>links, and highly non-independent and identically distributed (Non-IID) data. In particular, decentralized network to topologies exacerbate the difficulty of maintaining model consistency, thereby impairing overall learning performance. To address these challenges, we propose a new hierarchical decentralized federated learning (HDFL) framework. This framework combines the advantages of centralization and decentralization, builds a three-layer collaborative structure, and improves communication flexibility through an asynchronous model exchange mechanism between the edge and the client. Simultaneously, HDFL introduces a local fine-tuning strategy based on knowledge distillation<br/>to enhance the generalization ability and stability of the model. Experimental results using an urban traffic simulation platform show that HDFL consistently outperforms representative decentralized FL methods in terms of the achieved accuracy and convergence speed under heterogeneous IoV environments.<br/>Index Terms—decentralized federated learning, hierarchical federated learning, Internet of Vehicles, knowledge distillation