Seminar| Institute of Mathematical Sciences
Time: Friday, August 7th, 2026,15:00-16:00
Location: IMS RS408
Speaker: Mengchu Li, University of Birmingham
Abstract:Learning from distributed and heterogeneous data is central to modern data science. Recent advances in learning with multi-source data have shown that effectively integrating information across related datasets can significantly improve algorithmic performance. However, heterogeneity across datasets, in terms of sample size, distributional shift, and data quality, poses fundamental challenges in determining the optimal strategy for aggregating information. Moreover, sharing potentially sensitive information across distributed units raises serious privacy concerns.
In this talk, I will discuss two related projects addressing these challenges. The first studies federated transfer learning under privacy constraints. We introduce a notion of federated differential privacy, which protects each local data set without assuming a trusted central server, and characterise the statistical costs of privacy and heterogeneity across several statistical problems. The second project focuses on robust multi-task learning against adversarial contamination. We show that several existing regularisation-based approaches suffer from a dimension-dependent contamination error and are therefore statistically suboptimal. Motivated by this gap, we develop a computationally efficient filtering-based method that achieves near-optimal statistical performance over a broad range of model parameters.