Abstract: A defining trait of federated learning is the presence of heterogeneity, i.e., that data may differ between clients in the network. In this talk I discuss how heterogeneity affects issues of privacy and personalization in federated settings. First, I present our work on private personalized learning in cross-device settings, where we show that personalized FL provides unique benefits when enforcing client-level differential privacy in heterogeneous networks. Second, I explore cross-silo settings, where differences in privacy granularity introduce new dynamics in terms of the privacy/utility trade-offs of personalized FL. I end by discussing our application of these works to privacy-preserving pandemic forecasting in the recent UK-US privacy-enhancing technologies prize challenge, and highlight promising directions of future work on privacy and personalization in FL.

Bio: Virginia Smith is the Leonardo Assistant Professor of Machine Learning at Carnegie Mellon University. Her research spans machine learning, optimization, and distributed systems. Virginia’s current work addresses challenges related to optimization, privacy, and robustness in distributed settings to enable trustworthy federated learning at scale. Virginia’s work has been recognized by several awards, including an NSF CAREER Award, MIT TR35 Innovator Award, Intel Rising Star Award, and faculty awards from Google, Apple, and Meta. Prior to CMU, Virginia was a postdoc at Stanford University and received a Ph.D. in Computer Science from UC Berkeley.