VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI

TL;DR

This paper introduces VR-VFL, a novel vehicular federated learning method that adaptively manages client selection and transmission rates under imperfect wireless conditions, significantly improving convergence speed.

Contribution

It presents a bi-objective optimization framework for dynamic rate and client selection, addressing mobility and wireless challenges in vehicular federated learning.

Findings

Achieves approximately 40% faster convergence than existing methods.

Effectively balances learning convergence and round completion time.

Handles imperfect channel state information in vehicular networks.

Abstract

Federated learning in vehicular edge networks faces major challenges in efficient resource allocation, largely due to high vehicle mobility and the presence of imperfect channel state information. Many existing methods oversimplify these realities, often assuming fixed communication rounds or ideal channel conditions, which limits their effectiveness in real-world scenarios. To address this, we propose variable rate vehicular federated learning (VR-VFL), a novel federated learning method designed specifically for vehicular networks under imperfect channel state information. VR-VFL combines dynamic client selection with adaptive transmission rate selection, while also allowing round times to flex in response to changing wireless conditions. At its core, VR-VFL is built on a bi-objective optimization framework that strikes a balance between improving learning convergence and minimizing the time required to complete each round. By accounting for both the challenges of mobility and realistic wireless constraints, VR-VFL offers a more practical and efficient approach to federated learning in vehicular edge networks. Simulation results show that the proposed VR-VFL scheme achieves convergence approximately 40% faster than other methods in the literature.