FedDAF: Federated deep attention fusion for dangerous driving behavior detection

Dangerous driving behavior detection is one of the most important researches in Intelligent Transportation System (ITS), which can effectively reduce the probability and number of traffic accidents. Although some recent approaches combined with deep learning techniques have been proposed for detecting dangerous driving behaviors, the protection of user's privacy is neglected. Therefore, we propose a Federated Deep Attention Fusion model (FedDAF) to address the dual security issues in dangerous driving behavior detection, i.e., data security and traffic security. On the Client side, we design the Deep Attention Fusion Network for extracting and learning driving process features as well as fusing the environmental factors of the vehicle in driving. On the Server side, the Singular Spectrum Entropy Aggregation method is designed to aggregate Clients with high relevance and multiple information content, thereby realizing safety information sharing among Clients. Finally, the experimental results on real datasets show that the FedDAF method has the best performance on several evaluation metrics relative to the existing two categories of benchmark methods.