As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model
on a massive amount of devices with infrequent communication. This paper investigates a …

Communication efficiency and robustness are two major issues in modern distributed
learning frameworks. This is due to the practical situations where some computing nodes …

High-dimensional data is characterized by its sparsity and noise, which can increase the
likelihood of overfitting and compromise the model's generalizability performance. In this …

Statistical analysis of massive data is becoming more and more common. Distributed
composite quantile regression (CQR) for massive data is proposed in this paper …

Distributed statistical learning has gained significant traction recently, mainly due to the
availability of unprecedentedly massive datasets. The objective of distributed statistical …

The L p L^ p‐quantile regression generalizes both quantile regression and expectile
regression, and has become popular for its robustness and effectiveness especially when …

In large-scale distributed learning, the direct application of traditional inference is often not
feasible, because it may contain multiple themes, such as communication costs, privacy …

The debiased estimator is a crucial tool in statistical inference for high-dimensional model
parameters. However, constructing such an estimator involves estimating the high …

In this paper, we focus on robust sparse M-estimation over decentralized networks in the
presence of Byzantine attacks. In particular, a decentralized network is modeled as an …

We propose a new weighted average estimator for high-dimensional parameters under the
distributed learning system, where the weight assigned to each coordinate across different …