Aiming at the commonly existing malicious backdoor attacks in Federated Learning （FL） systems， and the difficulty of achieving a balance between high accuracy of privacy protection and model training in the existing defense schemes， the backdoor attacks and their defense methods in FL were explored， a safe and efficient integrated scheme called GKFL （Generative Knowledge-based Federated Learning） was proposed to detect backdoor attacks and repair damaged models. In this scheme， there was no need to access original privacy data of the participants， detection data were generated through the central server to detect whether the aggregation model in federal learning was backdoor attacked， and knowledge distillation technology was used to repair the damaged models， thereby ensuring integrity and accuracy of the models. Experimental results on datasets MNIST and Fashion-MNIST show that the overall performance of GKFL is better than that of classic schemes such as FoolsGold， GeoMed， and RFA （Robust Aggregation Algorithm）； GKFL can better protect data privacy than FoolsGold. It can be seen that GKFL scheme has the ability to detect backdoor attacks and repair the damaged models， and is better than the comparison schemes significantly in terms of model poisoning accuracy and the accuracy of model main task.