Скачать или смотреть T. Tony Cai: Federated Learning for Nonparametric Function Estimation:Framework&Optimality

Federated learning is a machine learning paradigm designed to tackle the challenges of data governance and privacy. It enables organizations (e.g., hospitals) to collaboratively train and enhance a shared global statistical model without sharing raw data externally. Instead, the learning process occurs locally at each participating entity, and only model characteristics, such as parameters and gradients, are exchanged, while preserving privacy. In this talk, we consider statistical optimality for federated learning in the context of nonparametric regression. The setting we study is heterogeneous, encompassing varying sample sizes and differential privacy constraints across different servers. Within this framework, both global and pointwise estimation are considered, and optimal rates of convergence over the Besov spaces are established. We propose distributed privacy-preserving estimation procedures and analyze their theoretical properties. The findings shed light on the delicate balance between accuracy and privacy preservation. In particular, we characterize the compromise not only in terms of the privacy budget but also concerning the loss incurred by distributing data within the privacy framework as a whole. This insight captures the folklore wisdom that it is easier to retain privacy in larger samples, and explores the differences between pointwise and global estimation under distributed privacy constraints.