When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

Apple ML Research · 1 day ago

Researchers developed an unlearning framework that identifies training data points with negligible influence on model outputs and removes them before the unlearning process. The method achieves approximately 50% computational savings on real-world examples by reducing dataset size prior to unlearning. This enables faster and more efficient removal of specific data from trained models while maintaining privacy compliance.