报告题目：Improving Training Efficiency and Quality in Federated Learning

报告时间：2023年4月18日16:00

报告地点：美高梅4688集团amB404

报告人：何黎刚

报告人国籍：英国

报告人单位：The University of Warwick

报告人简介：何黎刚，博士本科和硕士毕业于华中科技大学（硕士师从韩宗芬教授和金海教授）。博士毕业于英国华威大学计算机系，并在剑桥大学进行博士后研究。现为华威大学计算机系Reader（英国大学副教授和正教授之间的一个职称）。主要研究方向为并行分布式处理，分布式AI和大数据处理。在国际期刊和会议上发表论文180余篇，主持和承担过英国、欧盟及企业界多个研究项目。

报告摘要：In this talk, three piece of work we have conducted in FL will be presented. First, a semi-asynchronous FL protocol called SAFA is presented to improve the training efficiency of FL. Second, A FL scheme called hybridFL is presented. HybridFL further enhances the efficiency of SAFA by taking the reliability of FL clients into account. Moreover, hybridFL extends SAFA from a two-layer (i.e., client/server) FL scheme to a three-layer one in mobile-edge-cloud systems, enabling the support of even larger-scale FL training. Finally, in hybridFL, the clients are randomly selected to participate in FL training. If some clients have low quality data, the fact that they have equal opportunities to contribute to the final model may hurt the model quality. To address this issue, a selective FL scheme is proposed, in which data quality can be quantified and the clients with lower-quality data have fewer chances to be selected for training.

邀请人：杜博、郑志高