MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles

TL;DR

This paper introduces MOB-FL, a mobility-aware federated learning framework for connected vehicles that optimizes training parameters to improve convergence speed amid short-lived vehicle connections.

Contribution

It proposes a novel MOB-FL algorithm that enhances federated learning efficiency by considering vehicle mobility and connection constraints.

Findings

MOB-FL accelerates convergence in vehicular federated learning.

The framework improves resource utilization of ICVs.

Simulation results confirm the effectiveness of MOB-FL.

Abstract

Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage. However, due to the mobility of vehicles, the connections between the BS and ICVs are short-lived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process. In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL. We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under short-lived wireless connections, so as to increase the convergence speed. Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.