培训讲座前辈: 付华柱院士,添加坡科枝科研局 (A*STAR) 性能高参数计算方式出科研所 (IHPC) 专业科研员 (Senior Scientist)。关键科研方向盘为计算方式出机看上去,药学图面阐述,并且能信人员智慧等。至今为止已在 Nature Communications, IEEE TPAMI, IEEE TIP, IEEE TMI 等杂志和办公会议内容上先生发表综述170 余篇,Google Scholar 引述 1.4 万余次。曾获 2021 年 ICME 极佳选择综述奖、2022 年 MICCAI OMIA Workshop 极佳选择综述、Stanford 社会 Top 2% Scientists Worldwide等。现出任 IEEE TMI,IEEE TNNLS 和 IEEE JBHI 等杂志编委,并且多國際办公会议内容的地区新主席。时候也是 IEEE Bio Imaging and Signal Processing Technical Committee (BISP TC) 能力常务委员。 讲座m6米乐app 稿详细介绍: Federated learning (FL) is an emerging distributed machine learning paradigm that leverages decentralized data from multiple clients to jointly train a shared global model under the coordination of a central server, without sharing the individuals' data. This makes FL surpass traditional parallel optimization to avoid systemic privacy risk. In this talk, I will introduce several works on FL in healthcare. Moreover, I also discuss some open challenges for federated learning.