Dual-Layer Federated Learning Based Edge Collaborative Computing Mechanism for High Dynamic Internet of Vehicle Businesses
As an emerging distributed machine learning architecture,federated learning (FL) allows multiple users to train local models and achieve global aggregation of models with data privacy protection,thus providing reliable Internet of Vehicle (IoV) services. However,in the training process of FL,many training terminals may switch among domains due to the high mobility,resulting in low accuracy of the global model. Besides,malicious terminals may frequently upload invalid or incorrect model data which leads to low service reliability. Therefore,we build the dual-layer FL based edge collabora-tive computing mechanism for high dynamic IoV businesses. Firstly,we comprehensively consider the mobility,computing ability and reliability to construct the service capability model for the terminal,and then propose the edge collaborative com-puting domain (ECCD) construction algorithm based on deep reinforcement learning. By clustering the vehicle terminals covered by multiple edge nodes,the switching probability of the terminal local model will be reduced,and the sustainability of the FL model training can be guaranteed. Furthermore,we design a dual-layer FL framework including the inter-ECCD aggregation layer and cross-ECCD aggregation layer,respectively. It adopts the semi-asynchronous aggregation mechanism for local models based on the adaptive aggregation factor in the inter-ECCD aggregation layer,and the asynchronous aggre-gation mechanism for domain's regional model based on data volume in the cross-ECCD aggregation layer,which jointly improve the aggregation efficiency of the FL system. In particular,considering that the high speed terminals may cause the cross-domain problem,we introduce the partial conditional update mechanism for the local model to avoid the situation that the high-quality models are covered by the low-quality models,which further improves the accuracy of the global model and the utilization of FL system resources. The simulation results verify that the proposed framework outperforms the local computing and asynchronous/synchronous FL algorithms in terms of model accuracy and service reliability.
federated learningedge computingreliabilityhigh dynamicInternet of Vehicle
万方数据
维普
