提出横竖混合联邦学习框架，提升边缘物联网多设备协同效率。
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT
- 结合横向与纵向联邦学习，分设备处理相同样本或特征。
- 12横6纵配置下测试损失分别比反配置高5.5%和25.2%。
- 适合数据异构性强的边缘物联网场景，优化模型收敛性。
本文提出一种新型水平-垂直混合联邦学习（HoVeFL）框架，适用于移动边缘计算赋能的物联网（EdgeIoT）。该框架中，部分边缘物联网设备使用相同数据样本但分析不同数据特征，另一些设备则聚焦相同特征但使用非独立同分布（non-IID）的数据样本。因此，尽管数据特征一致，各设备间数据样本仍存在差异。所提HoVeFL通过最小化全局损失函数来联合训练本地与全局模型。在CIFAR-10与SVHN数据集上的性能评估表明，当配置为12个横向联邦学习设备与6个纵向联邦学习设备时，测试损失分别比配置为6个横向与12个纵向设备时高出5.5%和25.2%。
原文摘要 · Abstract (English)
This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices.
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