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

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.

💡 Research Summary

This paper tackles the practical challenges of deploying federated learning (FL) in vehicular edge networks where high mobility induces rapid channel variations and only imperfect channel state information (I‑CSI) is available. Existing works often assume idealized conditions such as fixed round durations, perfect CSI, or unlimited retransmissions, which are unrealistic for real‑world vehicular scenarios.

The authors introduce Variable‑Rate Vehicular Federated Learning (VR‑VFL), a framework that jointly selects participating vehicles and assigns individual transmission rates while allowing the duration of each FL round to adapt to current wireless conditions. The core contribution is a bi‑objective optimization problem that simultaneously (i) maximizes the expected reduction in the global loss function (by encouraging the inclusion of as many vehicles as possible) and (ii) minimizes the round time (by penalizing the lowest transmission rate among the selected clients). A weighting parameter α∈