MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
This work addresses convergence issues in federated learning for mobile vehicle networks, offering a domain-specific optimization that is incremental in nature.
The paper tackled the problem of slow convergence in federated learning for intelligent connected vehicles due to short-lived wireless connections from vehicle mobility, and proposed MOB-FL to optimize training round duration and local iterations, resulting in improved convergence speed verified by simulations on beam selection and trajectory prediction tasks.
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.