Joint resource management for mobility supported federated learning in Internet of Vehicles

In recent years, the powerful combination of Multi-access Edge Computing (MEC) and Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent Transportation Systems (ITS). However, there is a mismatch between the ever-increasing consumer privacy awareness and the data leakage risk in centralized AI training solutions in vehicular edge scenarios, which has become a new obstacle to satisfying the user experience. As a promising privacy-preserving paradigm, federated learning synthesizes a global model only with the parameters of decentralized trained local models, avoiding the exposure of sensitive data. Given this, we introduce federated learning into the proposed two-level MEC-assisted vehicular network framework. This paper aims to address the challenges posed by adopting federated learning into the Internet of Vehicles (IoV) scenario. Firstly, as the entity of the participant (the local model training node of federated learning), vehicles have high mobility. We design a mobility supported federated learning participant decision algorithm to pick out participants from candidate vehicles. Secondly, federated learning is rather resource-consuming, inevitably incurring considerable costs to participants. We focus on the joint resource allocation problem to optimize the federated learning cost. Finally, considering the limitations of centralized resource allocation, we propose a fully distributed resource allocation method inspired by multiagent deep reinforcement learning. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed schemes. (C) 2021 Published by Elsevier B.V.