联合数据质量评估方法:强大的学习与混合标签噪声
IEEE transactions on neural networks and learning systems
|August 31, 2023
概括
混合标签噪音的联合学习 (FL) 被FedMIN解决,FedMIN是一种新的方法,通过识别噪音类型和调整聚合来稳健训练模型. 这种方法在分布式设置中显著提高了全球模型的准确性.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 允许在不共享本地数据的情况下进行分布式模型培训.
- 标签噪声是FL的一个重大挑战,原因是局部数据难以获得.
- 现有的方法往往无法解决现实世界中常见的混合噪声类型.
研究的目的:
- 提出一种新的FL方法,FedMIN,用于在混合标签噪声的情况下进行强有力的训练.
- 开发一个能够区分噪声类型和提高模型性能的框架.
- 在杂的数据条件下提高联合模型的准确性和可靠性.
主要方法:
- 通过捕捉局部-全球分布差异,FedMIN利用一个复合框架来建模通用噪声模式.
- 确定适应性值,以识别每个客户端的混合标签噪声.
- 在模型聚合过程中分配适当的权重,并使用高斯混合模型 (GMMs) 纳入损失对齐机制.
主要成果:
- 费德MIN展示了卓越的噪声估计能力,从而提高了全球模型性能.
- 对模拟 (CIFAR-10,CIFAR-100,SVHN) 和真实 (Camelyon17,MoNuSAC) 数据集的实验显示了显著的准确性增长.
- 与现有的FL基准相比,FedMIN在模型准确度上实现了高达9.9%的改进.
结论:
- 在联合学习中,FedMIN有效地解决了混合标签噪声的挑战.
- 拟议的方法提高了分布式,杂的环境中联合模型的稳定性和准确性.
- 对于数据质量变化的现实应用,FedMIN提供了一个有前途的解决方案.
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