報告題目：Improving Training Efficiency and Quality in Federated Learning

報告時間：2023年4月18日16:00

報告地點：437bwin必贏國際官網B404

報告人：何黎剛

報告人國籍：英國

報告人單位：The University of Warwick

報告人簡介：何黎剛，博士本科和碩士畢業于華中科技大學（碩士師從韓宗芬教授和金海教授）。博士畢業于英國華威大學計算機系，并在劍橋大學進行博士后研究。現為華威大學計算機系Reader（英國大學副教授和正教授之間的一個職稱）。主要研究方向為并行分布式處理，分布式AI和大數據處理。在國際期刊和會議上發表論文180余篇，主持和承擔過英國、歐盟及企業界多個研究項目。

報告摘要：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.

邀請人：杜博、鄭志高