Improving Unlearning with Model Updates Probably Aligned with Gradients
This work addresses the problem of efficiently removing data from trained models for privacy or compliance, presenting an incremental improvement to existing approximate unlearning methods.
The paper tackles the machine unlearning problem by formulating it as a constrained optimization and introducing feasible updates based on parameter masking and gradient noise estimation, which can be added to existing first-order methods. Experiments with computer vision classifiers validate the approach, though no concrete performance numbers are provided.
We formulate the machine unlearning problem as a general constrained optimization problem. It unifies the first-order methods from the approximate machine unlearning literature. This paper then introduces the concept of feasible updates as the model's parameter update directions that help with unlearning while not degrading the utility of the initial model. Our design of feasible updates is based on masking, \ie\ a careful selection of the model's parameters worth updating. It also takes into account the estimation noise of the gradients when processing each batch of data to offer a statistical guarantee to derive locally feasible updates. The technique can be plugged in, as an add-on, to any first-order approximate unlearning methods. Experiments with computer vision classifiers validate this approach.