FedMPS:基于多层次原型的对比学习和软标签生成的协同作用中的联合学习
IEEE transactions on neural networks and learning systems
|October 6, 2025
概括
联合学习 (FL) 与数据异质性作斗争. 通过使用多级原型和软标签,FedMPS增强了FL,提高了模型性能并降低了通信成本.
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:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模式培训,从而保护用户的隐私.
- 在FL的客户端中数据异质性引入了偏差,降低了本地模型性能,并减缓了融合.
- 目前的FL方法在有效的知识传输方面面临挑战,导致高通信开销和低于最佳的结果.
研究的目的:
- 提出一个新的联邦学习框架,FedMPS,它解决了数据异质性和通信效率.
- 通过整合基于多级原型的对比学习和软标签生成来增强协作学习.
- 为了减少全球知识转移和通信成本在FL.
主要方法:
- 从不同的模型层构建多层原型,以捕捉高层语义和低层细节.
- 在这些原型中使用对比式学习 (CL) 来提高功能空间的可区分性和一致性.
- 引入一个以原型为导向的软标签生成模块,以模拟输出空间中的类间关系.
主要成果:
- 通过仅传输原型和软标签,而不是模型参数,FedMPS有效地降低了通信成本.
- 拟议的方法证明了在特征空间中提高了类内可区分性和一致性.
- 六个数据集的实验结果表明,FedMPS的表现优于最先进的FL方法.
结论:
- 在数据异质性下,FedMPS为联合学习提供了有效的解决方案.
- 与现有的FL方法相比,该框架实现了更好的性能和效率.
- 传输原型和软标签是FL知识共享的可行策略.
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