Hierarchical federated learning with mobile edge computing in the Internet of Vehicles

Abstract

Federated Learning is a distributed machine learning framework, which can be used in the Internet of Vehicles to train deep learning models without directly accessing the original data of mobile edge vehicle nodes. ECS can access massive data, but it has the characteristics of high latency and high communication overhead. However, mobile edge computing (MEC) platform can directly and efficiently communicate with mobile edge vehicle nodes. Combining the advantages of the two, a three-layer federated learning system of edge car network edge server cloud server is used. This system is supported by the HierFedProx algorithm and aggregates the model output of the edge car to the edge server to improve the model learning efficiency and reduce the global communication frequency. The experimental results show that the system can reduce the training time and improve the accuracy of the model compared with the federated learning without introducing the edge server.