动态客户端蒸半监督联合学习在一个现实的场景
IEEE transactions on medical imaging
|May 14, 2025
概括
本研究介绍了FedCD,这是一个新的半监督联合学习 (SSFL) 框架,用于现实的医疗数据场景. FedCD有效地利用未标记的数据,并适应不同的注释级别,改善了联合学习中的模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 半监督联合学习 (SSFL) 通过允许医疗数据共享,促进公共卫生.
- 现有的SSFL方法假设统一的数据标签 (服务器/客户端标签),这对于各种医疗机构来说是不现实的.
研究的目的:
- 开发一个新的SSFL框架 (FedCD),用于现实的客户数据场景,具有不同的注释级别 (完全标记,部分标记,完全无标记).
- 在客户端联盟中最大限度地利用未标记数据,并适应异质数据分布.
主要方法:
- 提出了FedCD框架,其中有三个客户端提炼的模型,用于不同的数据分布.
- 雇佣了服务器-客户端联合和知识蒸用于参数凝聚.
- 实施了基于接近蒸模型的客户模型贡献的动态调整.
- 使用聚合客户蒸模型进行模型漂移校正.
主要成果:
- FedCD有效地利用未标记的数据,并容纳不同的注释级别.
- 该框架适应不同的数据分布,并减轻异质模型中的参数漂移.
- 在两个医学图像细分和一个分类任务中表现出优越性.
结论:
- 通过整合不同的注释级别和数据分布,FedCD解决了医疗数据场景中的现实挑战.
- 动态联合方法提高了SSFL在医疗保健应用中的效率和适应性.
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