GCN-Based Topology Design for Decentralized Federated Learning in IoV

Decentralized federated learning (DFL) is a promising technology to implement distributed machine learning in Internet of Vehicles (IoV), which enables vehicles to share and aggregate models with their neighbors in a vehicle-to-vehicle (V2V) network. However, due to the high mobility of vehicles, model sharing via V2V links may fail as the topology of the V2V network is time-varying, which greatly reduces the efficiency of model aggregating and the speed of model training. To address this problem, in this paper, we propose a graph convolution network (GCN)-based topology design method, named G-DFL, to improve the training efficiency of DFL in IoV by properly selecting a subgraph of the underlay V2V network, which is referred to as overlay network, in each round of model sharing. First, by encoding the state of vehicles, we utilize a GCN to extract the features of V2V network topology to predict the effective V2V links for model sharing. In addition, to further reduce the delay of model training, we use Christofides' Algorithm to find the Hamiltonian circuit with the least delay as the overlay network. Simulation results validate that the proposed method significantly improves the model training performance in DFL compared with the other baseline methods.