A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

Autor:	Li, Kai, Liang, Yilei, Yuan, Xin, Ni, Wei, Crowcroft, Jon, Yuen, Chau, Akan, Ozgur B.
Rok vydání:	2024
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.01644 Zobrazit plný text záznamu View this record from Arxiv