AuthorsAnat Kleiman†**, Robert Fisher, Ben Deaner‡, Udi Wieder, Vitaly Feldman
As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking: do points that have a negligible impact on the model’s learning need to be removed? Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs. Leveraging this insight, we propose an efficient unlearning framework that reduces the size of datasets before unlearning—leading to significant computational savings (up to ~50%) on real-world empirical examples.
Apple believes that privacy is a fundamental human right. As AI experiences become increasingly personal and a part of people’s daily lives, it’s important that novel privacy-preserving techniques are created in parallel to advancing AI capabilities.
Apple’s fundamental research has consistently pushed the state-of-the-art in using differential privacy with machine learning, and earlier this year, we hosted the Workshop on Privacy-Preserving…
*= Equal Contributions
Recovering linear subspaces from data is a fundamental and important task in statistics and machine learning. Motivated by heterogeneity in Federated Learning settings, we study a basic formulation of this problem: the principal component analysis (PCA), with a focus on dealing with irregular noise. Our data come from nn users with user ii contributing data samples from a dd-dimensional distribution with mean μi\mu_i…
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