统计与数据科学系系列学术报告之五百零四期
时 间：2026年3月13日（星期五）16:00-17:00
主持人：复旦大学 管理学院 统计与数据科学系 朱仲义 教授
地 点：史带楼302室
报 告 人：张宝学教授 首都经济贸易大学
题 目：A locally sequentially reweighted gradient descent estimator to enhance statistical efficiency for decentralized federated learning
摘 要：While many studies have considered the numerical convergence of federated learning algorithms, far less attention has been given to their statistical convergence. In this paper, to enhance statistical efficiency, we propose a novel Locally Sequentially Re-weighted Gradient Descent (LSRGD) estimator for decentralized federated learning. Furthermore, we prove that the LSRGD estimator is asymptotically normal and achieves optimal statistical efficiency. Moreover, we also propose a parallel version of the LSRGD algorithm, referred to as LSRGD-P. Finally, extensive experiments demonstrate that LSRGD and LSRGD-P estimators exhibit superior statistical efficiency compared to existing competitors. This advantage is particularly pronounced in scenarios where the data across different clients are imbalanced.
个人简介：张宝学教授是首都经济贸易大学统计学院教授，博士生导师。中国现场统计学会副理事长、全国应用统计专业学位研究生教育指导委员会委员、中国统计教育学会高等教育分会秘书长。
统计与数据科学系
2026-3-10
-
活动讲座
-
新闻动态
-
微信头条
-
招生咨询
-
媒体视角
-
瞰见云课堂
