Research on asynchronous robust federated learning method in vehicle computing power network

The synchronous training mechanism of traditional federated learning was not suitable for dynamic vehicle computing power network scenarios, and lacked effective detection mechanisms under the threat of malicious vehicle attacks. To address the above issues, an asynchronous robust federated learning method was proposed, which achieves vehicle data privacy protection while improving the efficiency of model collaborative training through asynchronous execution of federated learning processes between vehicles. Secondly, a model selection method was designed, and potential malicious model detection and vehicle reputation evaluation methods are proposed to further enhance the robustness of the system. Then, the safety of the proposed method was analyzed in detail from a probabilistic perspective, providing a theoretical basis for optimizing various parameters. Finally, the simulation results show that this method can achieve efficient asynchronous federated learning while having good robustness.