查看文章

In the heterogeneous vehicular networks (HetVNets), the roadside units (RUs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently request their model training tasks in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-RU and propose a digital twins (DT) enabled on-demand matching scheme for multi-task federated learning to address the two-way selection problem between TRs and RUs. Specifically, by jointly considering the diversified requirements of the TRs and the differentiated training capabilities of the RUs, we first design a DT enabled on-demand matching architecture to facilitate the multi-task federated learning in the HetVNets. Then, based on the personalized requirement of the DT …