Robust Personalized Federated Learning with Sparse Penalization

• 成果类型: Article
• 署名作者: Liu, Weidong; Mao, Xiaojun; Zhang, Xiaofei; Zhang, Xin
• 署名单位: Shanghai Jiao Tong University; Shanghai Jiao Tong University; Zhongnan University of Economics & Law; Iowa State University; Zhongnan University of Economics & Law
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2321652
• 发表日期: 2025
• 页码: 266-277
• 关键词: Quantile regression; variable selection; M-ESTIMATORS; nonconvex; RECOVERY

摘要

Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important to consider personalization when developing federated learning methods. In this work, we propose a personalized federated learning (PFL) method to address the robust regression problem. Specifically, we aim to learn the regression weight by solving a Huber loss with the sparse fused penalty. Additionally, we designed our personalized federated learning for robust and sparse regression (PerFL-RSR) algorithm to solve the estimation problem in the federated system efficiently. Theoretically, we show that the proposed PerFL-RSR reaches a convergence rate of O(1/T), and the proposed estimator is statistically consistent. Thorough experiments and real data analysis are conducted to corroborate the theoretical results of our proposed personalized federated learning method. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.