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

2026-02-03

AI Summary

This work addresses the challenge of inefficient resource allocation in vehicular edge networks caused by high mobility and imperfect channel state information (CSI) in federated learning. To tackle this issue, the authors propose VR-VFL, a novel approach that jointly optimizes dynamic client selection and adaptive transmission rates within a variable communication round duration framework, tailored to realistic wireless conditions and vehicle mobility. Built upon a bi-objective optimization framework, VR-VFL explicitly models imperfect CSI to minimize round latency while ensuring learning convergence. Experimental results demonstrate that VR-VFL achieves approximately 40% faster convergence compared to existing methods.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Vehicular Federated Learning
• Imperfect CSI
• Resource Allocation
• High Mobility
• Communication Efficiency

Innovation

• Vehicular Federated Learning
• Imperfect CSI
• Dynamic Client Selection
• Adaptive Transmission Rate
• Bi-objective Optimization