FedParking：一种基于联邦学习的停车位估计方法，停车车辆辅助边缘计算
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
摘要：
作为一种分布式学习方法，联邦学习在分布式数据集上训练共享的学习模型，同时保护训练数据的隐私。我们将联邦学习的应用扩展到停车管理，并引入FedParking，其中停车场运营商（PLO）合作训练一个长短期记忆模型，用于停车位估计，而无需交换原始数据。此外，我们还研究了FedParking对停放车辆辅助边缘计算（PVEC）的管理。在PVEC中，不同的PLO通过激励机制招募PV作为边缘计算节点来卸载服务，该激励机制根据FedParking导出的计算需求和停车容量约束来设计。我们制定的PLO和车辆之间的相互作用作为一个多领导多追随者Stackelberg游戏。考虑到车辆的动态到达和随时间变化的停车容量约束，我们提出了一种多智能体深度强化学习方法，以分布式但隐私保护的方式逐渐达到Stackelberg均衡。最后，数值结果证明了我们的计划的有效性和效率。
英文摘要：
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw data. Furthermore, we investigate the management of Parked Vehicle assisted Edge Computing (PVEC) by FedParking. In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking. We formulate the interactions among the PLOs and vehicles as a multi-lead multi-follower Stackelberg game. Considering the dynamic arrivals of the vehicles and time-varying parking capacity constraints, we present a multi-agent deep reinforcement learning approach to gradually reach the Stackelberg equilibrium in a distributed yet privacy-preserving manner. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
