通过个性化进行联合原型校正来解决歪曲的异质性
IEEE transactions on neural networks and learning systems
|August 27, 2024
概括
联合学习 (FL) 通过实现协作培训来解决数据隐私问题. 本研究介绍了倾斜异质FL (SHFL) 和一种新方法,FedPRP,以改善不平衡数据集的模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 允许跨分布式设备进行协作模式培训,同时保持数据隐私.
- 在 FL 中的一个关键挑战是数据层次的异质性,其特点是跨客户端的数据分布偏斜或长尾.
- 现有的FL方法通常假设统一的数据分布,这在实际情况下是不现实的.
研究的目的:
- 在数据层次异质性下研究联合学习,特别是在更实用的倾斜异质FL (SHFL) 设置中.
- 提出一种新的方法,即带有个性化的联合原型修正 (FedPRP),以应对SHFL的挑战.
- 在基于偏斜数据的联合学习模型中增强个性化和泛化能力.
主要方法:
- 该研究将问题设置重新定义为倾斜异质FL (SHFL).
- 提出了一种新的方法,FedPRP,由两个组成部分组成:联合个性化和联合原型校正.
- 联合个性化旨在为主导阶级和少数阶级创造平衡的决策边界,而联合原型校正则使用阶级间和阶级内部信息来改进实证原型.
主要成果:
- 三个基准数据集的实验结果证明了拟议的FedPRP方法的有效性.
- 在联邦学习中,FedPRP的性能优于现有的最先进的方法来处理偏斜的数据分布.
- 该方法在个性化和概括之间实现了平衡的性能.
结论:
- 拟议的FedPRP方法有效地解决了联合学习中数据层次异质性的挑战,特别是在SHFL环境中.
- FedPRP提供了一种实际的解决方案,用于改善现实世界联合学习应用程序中的模型性能和公平性,这些应用程序具有不均衡的数据.
- 这项工作推进了联合学习领域,为偏斜的数据分布提供了一个强大的方法.
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