Federated learning enables a large amount of edge computing devices to jointly learn a
model without data sharing. As a leading algorithm in this setting, Federated Averaging …

Modern large-scale machine learning applications require stochastic optimization
algorithms to be implemented on distributed computational architectures. A key bottleneck is …

The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

This paper studies hypothesis testing and parameter estimation in the context of the divide-
and-conquer algorithm. In a unified likelihood based framework, we propose new test …

Data are distributed across different sites due to computing facility limitations or data privacy
considerations. Conventional centralized methods—those in which all datasets are stored …

Quantile regression under memory constraint Page 1 The Annals of Statistics 2019, Vol. 47,
No. 6, 3244–3273 https://doi.org/10.1214/18-AOS1777 © Institute of Mathematical Statistics …

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …

The development of models and methodology for the analysis of data from multiple
heterogeneous networks is of importance both in statistical network theory and across a …

When the data are stored in a distributed manner, direct applications of traditional statistical
inference procedures are often prohibitive due to communication costs and privacy …