FedVANET: Efficient Federated Learning with Non-IID Data for Vehicular Ad Hoc Networks
Beibei Li, Yukun Jiang, Wen–Bin Sun, Weina Niu, Peiran Wang
Abstract
The vehicular ad hoc networks (VANETs) play a significant role in intelligent transportation systems (ITS). In recent years, federated learning (FL) has been widely used in VANETs to preserve the privacy-sensitive data, such as vehicle locations, drivers' driving patterns, on-board camera data, etc. However, conventional FL faces the challenges of non-independent and identically distributed (Non-IID) data and high communication overheads in VANETs. To address these challenges, we propose a novel FL framework for VANETs, named FedVANET, where a hierarchical inner-cluster FL model and a weighted inter-cluster cycling update algorithm are, respectively, developed. Extensive experiments demonstrate the high efficiency of the FedVANET in inner-cluster communications, effectiveness in handling Non-IID data, and robustness in dynamic VANET topologies.