报告题目： Adaptive distributed learning system with privacy preservation
报告人：林绍波 教授 西安交通大学
报告时间：2021年11月25日（星期四） 09:00-11:00
报告地点：腾讯视频会议（线上） ID: 580 273 801
校内联系人：徐敏 副教授 联系电话：84708351-8101
报告摘要： In this talk, we propose a novel adaptive distributed learning system based on divide-and-conquer and local average regression for prediction and privacy preservation simultaneously. Different from the classical distributed learning strategy whose algorithmic parameters and patterns are given by the central agent, our approach provides autonomy to each local agent in terms of parameter selection, algorithm designation and data perturbation. Such an adaptive manner significantly enhances the privacy preservation of the system. Our theoretical results demonstrate that the novel adaptive distributed learning system does not degrade the prediction performance of classical systems via presenting optimal learning rates in the framework of statistical learning theory. Our theoretical assertions are verified via numerous numerical experiments including both toy simulations and real data study. In our analysis, the new system also admits a certain perturbation of the test data via showing an almost comparable accuracy to that of the original data, which provides a realistic possibility for protecting privacy from both training and testing sides.
报告人简介：林绍波，西安交通大学管理学院，教授、博士生导师。研究方向为函数逼近论、分布式学习理论、深度学习理论及强化学习理论。在应用数学顶级期刊ACHA、SINUM、CA及机器学习顶级期刊JMLR，TPAMI，TIT等发表论文70余篇。主持或以核心骨干参与国家级课题11项。
