Fig. 5: Performance of forgetting using AFS on four datasets.

a The p value of auditing on a small query dataset and a large query dataset (QF) and the accuracy of models trained with different methods, including Original (Independent teacher trained with the complete training dataset), SISA, CF-k, EU-k, Data Deletion (the Independent student model trained with partial training dataset and k = 0.5), AFS (w/o Audit) and AFS. p values were calculated using two-tailed Student’s t test. b The number of parameters for the original large model and the new small model generated by AFS. c The qualitative evaluation of three methods, including Original (Independent teacher trained with the complete training dataset), SISA, CF-k, EU-k, Data Deletion (the Independent student model trained with partial training dataset and k = 0.5), and AFS on five dimensions (Ability to forget, accuracy, size of dataset needed for training, size of the generated model and the efficiency of training). A larger value means a stronger ability to forget, higher model accuracy, a smaller size of dataset needed for training, a smaller size of the generated model, and better efficiency of training. d Illustration of how to incorporate AFS into real-world applications. The brain icons in d are Designed by macrovector/Freepik.