FLCMC:中医药文本分类的联合学习方法
1School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了FLCMC,这是一种用于中国医学文本分类的联合学习方法,增强了隐私和数据共享. 对于异质数据,FedPA和FedPAP算法提高了准确性和稳定性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 联合学习在医疗文本等敏感领域面临数据异质性和隐私方面的挑战.
- 现有的联合算法在各种数据集中努力保持性能和稳定性.
研究的目的:
- 为中国医学文本分类提出新的联合学习算法 (FedPA,FedPAP).
- 为解决中国医学文本分析中的隐私保护和数据共享问题.
- 在异质数据上提高联合学习的性能和趋同稳定性.
主要方法:
- 引入了FLCMC,一种结合式学习方法,包含自我注意机制.
- 开发了带有定制优化器的扰乱联合学习算法 (FedPA,FedPAP).
- 在模型聚合中集成自我注意,并在本地更新中添加扰动术语.
主要成果:
- 在合成和真实世界的中国医疗数据集 (IMCS-V2) 上,FedPA和FedPAP表现出卓越的准确性和趋同稳定性.
- 拟议的算法表现优于FedAvg,FedProx和FedAtt,特别是在异质数据方面.
- 在中国医学文本的深度学习模型上取得了强大的概括能力.
结论:
- 拟议的FedPA和FedPAP算法有效地处理中国医学文本分类中的数据异质性.
- 这些方法为医疗NLP任务提供了更好的隐私,数据共享,准确性和稳定性.
- FLCMC为分析敏感的中国医学文本数据提供了强大的解决方案.
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