FedOcw:优化联合学习,用于跨语言基于语音的帕金森病检测
Changqin Quan1, Zhonglue Chen2, Kang Ren2
1Graduate School of System Informatics, Kobe University, Kobe, Japan. quanchqin@gold.kobe-u.ac.jp.
NPJ digital medicine
|June 13, 2025
概括
本研究介绍了FedOcw,这是一个新的联合学习 (FL) 框架,用于使用语音分析检测帕金森病 (PD). FedOcw 提高了跨语言知识传输和模型稳定性,在各种环境中优于传统的 FL 方法.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 语音分析显示了早期帕金森病 (PD) 检测和管理的前景.
- 开发精确的机器学习模型受到分散,异构和多语言PD语音数据的阻碍.
- 传统的联合学习 (FL) 方法在医疗数据中常见的非独立和相同分布 (非IID) 环境中面临挑战.
研究的目的:
- 提出FedOcw,一个优化的FL框架,以增强PD语音分析的跨语言知识转移和融合稳定性.
- 解决传统FL在异质,多语言数据集中对帕金森病检测的局限性.
主要方法:
- 开发了FedOcw,一个优化的联合学习框架.
- 进行了广泛的多语言实验来评估FedOcw的性能.
- 专注于加强跨语言知识转移和在非IID环境中的融合稳定性.
主要成果:
- 在诊断准确度方面,FedOcw始终优于传统的FL模型.
- 在各种语言和机构环境中表现出卓越的表现.
- 确保在联合网络中的客户之间进行适应性和公平的权重分配.
结论:
- FedOcw是一个有效的FL解决方案,用于保护隐私,基于语音的帕金森病检测.
- 该框架增强了跨语言知识传输和在异质数据设置中的模型稳定性.
- 突出了优化FL在不同人群中的实际临床应用的潜力.
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