$\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning
Daniel Trippa Cesare Campagnano Maria Sofia Bucarelli Gabriele Tolomei Fabrizio Silvestri
Machine Unlearning, the process of selectively eliminating the influence of
certain data examples used during a model's training, has gained significant
attention as a means for practitioners to comply with recent data protection
regulations. However, existing unlearning methods face critical drawbacks,
including their prohibitively high cost, often associated with a large number
of hyperparameters, and the limitation of forgetting only relatively small data
portions. This often makes retraining the model from scratch a quicker and more
effective solution. In this study, we introduce Gradient-based and
Task-Agnostic machine Unlearning ($\nabla \tau$), an optimization framework
designed to remove the influence of a subset of training data efficiently. It
applies adaptive gradient ascent to the data to be forgotten while using
standard gradient descent for the remaining data. $\nabla \tau$ offers multiple
benefits over existing approaches. It enables the unlearning of large sections
of the training dataset (up to 30%). It is versatile, supporting various
unlearning tasks (such as subset forgetting or class removal) and applicable
across different domains (images, text, etc.). Importantly, $\nabla \tau$
requires no hyperparameter adjustments, making it a more appealing option than
retraining the model from scratch. We evaluate our framework's effectiveness
using a set of well-established Membership Inference Attack metrics,
demonstrating up to 10% enhancements in performance compared to
state-of-the-art methods without compromising the original model's accuracy.