纵向联邦学习(vertical federated learning,VFL)常用于高风险场景中的跨领域数据共享,用户需要理解并信任模型决策以推动模型应用。现有研究主要关注VFL中可解释性与隐私之间的权衡,未充分满足用户对模型建立信任及调优的需求。为此,提出了一种基于人在回路(human-in-the-loop,HITL)的纵向联邦学习解释方法(explainable vertical federated learning based on human-in-the-loop,XVFL-HITL ...
李晓欢, 郑钧柏, 康嘉文, 叶进, 陈倩
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概述了联邦可视化的概念、框架、方法与应用。联邦可视化框架能够在不进行数据整合的情况下,针对具体任务和特定场景进行加密训练,得出反映全体数据特征的可视化模型。联邦可视化是联邦学习框架在可视化领域的拓展应用,主要强调在保障数据隐私的前提下,互利共赢的联邦协作方式在对多数据源数据进行可视分析方面的应用,以打破各领域、各行业的数据壁垒,实现数据与知识的共享。
魏雅婷, 王智勇, 周舒悦, 陈为
doaj
[A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction]. [PDF]
Han C+5 more
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[Research progress on medical image dataset expansion methods]. [PDF]
Chen Y+7 more
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[CT and MRI fusion based on generative adversarial network and convolutional neural networks under image enhancement]. [PDF]
Liu Y+11 more
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[Guiding opinions on clinical application of OTO'VIEW technology]. [PDF]
Hou Z, Ma X, Wang F.
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Tracking public opinion about online education over COVID-19 in China. [PDF]
Zhou M, Mou H.
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[Early effectiveness of unilateral biportal endoscopic discectomy combined with annulus fibrosus suture in the treatment of lumbar disc herniation]. [PDF]
Zhu C+5 more
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[Application of posterior median longitudinal W-shaped incision combined with layer-by-layer combing suture in acute closed Achilles tendon rupture]. [PDF]
Wu C, Ding X, Wang Z, Ren S, Liu C.
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