Heterogeneity and Privacy in Modern Learning

This talk studies privacy-preserving learning when heterogeneity is part of the problem's structure. I will begin with recent work on private personalized learning, which develops a taxonomy of privacy notions for multitask learning and meta-learning, and proves separations between them. These results show that modeling privacy can fundamentally change what we can and cannot do in learning. I will then briefly discuss other forms of heterogeneity that fit within this perspective, particularly settings where privacy requirements differ across the data. This includes feature-specific privacy and heterogeneous local differential privacy, where privacy guarantees vary across coordinates or users. Taken together, these works suggest a broader message: meaningful privacy guarantees must adapt to the heterogeneous structure of modern learning problems.