In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide ef...In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.展开更多
基金supported by the National Natural Science Foundation of China under Grant 61931005Beijing Natural Science Foundation under Grant L202018the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001。
文摘In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.
