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.

翻译：本文提出了一种面向移动边缘计算赋能物联网（EdgeIoT）的新型横向-纵向混合联邦学习（HoVeFL）框架。在该框架中，部分EdgeIoT设备使用相同数据样本但分析不同数据特征来训练本地模型，而其他设备则使用非独立同分布（non-IID）数据样本专注于相同特征。因此，即使数据特征一致，不同设备间的数据样本也存在差异。所提出的HoVeFL通过构建本地模型与全局模型的训练过程，以最小化全局损失函数。在CIFAR-10和SVHN数据集上的性能评估表明：采用12个横向联邦学习设备与6个纵向联邦学习设备的HoVeFL配置，其测试损失相较于采用6个横向联邦学习设备与12个纵向联邦学习设备的配置，分别高出5.5%和25.2%。