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

• URL: http://arxiv.org/abs/2212.03519v1
• Date: Wed, 7 Dec 2022 08:53:53 GMT
• Title: MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
• Authors: Bowen Xie, Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Yang Xu, Jingran Chen, Deniz G\"und\"uz
• Abstract summary: We consider a base station coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner. Due to the mobility of vehicles, the connections between the base station and ICVs are short-lived. We propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations.
• Score: 21.615151912285835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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