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在基础模型中的双快速个性化联合学习

Ying Chang¹, Xiaohu Shi^2,3, Xiaohui Zhao¹

¹College of Software, Jilin University, Changchun, 130012, China.

Scientific reports

|August 1, 2025

概括

双快速个性化联合学习 (DP2FL) 通过整合基础模型来提高有限数据的模型性能. 这种框架允许无添加新客户和预测新数据源,而无需重新培训.

科学领域:

人工智能的人工智能
机器学习机器学习
计算机科学计算机科学

背景情况:

个性化联合学习 (PFL) 解决了数据异质性和隐私问题,但在有限的本地数据方面存在困难.
像CLIP这样的基础模型为有限的数据场景提供了强大的特征提取,但在联合设置中未得到充分利用.
整合新客户和对新数据源进行预测仍然是PFL尚未解决的挑战.

研究的目的:

提出一个新的框架,双快速个性化联合学习 (DP2FL),以克服PFL的局限性,缺乏足够的本地数据.
增强模型的概括性和适应性,以在联合环境中的多样化数据分布.
实现对新数据源的预测和无集成新客户,而无需完全再培训.

主要方法:

引入了一个双提示个性化联合学习 (DP2FL) 框架,包含双提示和自适应聚合策略.
结合全球任务意识与本地数据驱动的洞察力,用于模型培训.
开发了一个用于预测新数据源和客户端集成的全球模型.

主要成果:

DP2FL在高度异质的环境中表现出有效的概括性和适应性.
迅速设计和聚合策略被证明是有效的,验证了框架的方法.
该框架成功地实现了对新数据源的预测和新客户的无集成.

结论:

通过利用基础模型,DP2FL有效地解决了在个性化联合学习中有限的本地数据的挑战.
拟议的双快速和自适应性聚合策略提高了模型性能和适应性.
DP2FL提供了一个可扩展的解决方案,用于整合新的客户端,并利用新的数据源在联合学习系统.

关键词:

适应性聚合策略适应性聚合策略客户异质性的客户异质性基金会模型基金会模型个性化联合学习个性化联合学习

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