基于动态参数融合和原型对齐的个性化联合学习
Ying Chen1, Jing Wen2, Shaoling Liang2
1School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
联邦学习与非ID数据扎. FedDFPA是一个个性化的框架,使用动态参数融合和原型对齐来提高概括性和平衡个性化与协作.
科学领域:
- 人工智能
- 机器学习
- 分布式系统
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模型培训.
- 在FL中,普遍化仍然是一个挑战,尤其是在非独立且同样分布的 (非IID) 数据中.
- 现有的FL方法往往难以平衡全球模型性能与个体客户需求.
研究的目的:
- 提出一个新的个性化联合学习框架.
- 在非IID数据下解决FL的概括限制.
- 在联合学习系统中增强个性化和协作.
主要方法:
- 开发了FedDFPA,集成了动态参数融合和原型对齐.
- 实现了类智能动态参数融合机制,用于全球和本地分类器参数的自适应合并.
- 使用全球和历史数据引入原型对齐机制,以提高语义一致性和特征稳定性.
主要成果:
- 与最先进的算法相比,FedDFPA的平均测试准确度显著提高.
- 在实际的异质环境中获得3.59%的准确性改进.
- 在病理异质设置中获得了4. 71%的准确性改善.
结论:
- FedDFPA有效地减轻了非IID数据的联合学习中的泛化问题.
- 这种双重机制的设计成功地平衡了个性化和协作.
- 该框架为分布式环境中的个性化分类提供了强大的解决方案.
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