A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 2 Oct 2024.
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References · 16
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- 2023A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extractioncited by 12
- 2022Vertical Federated Edge Learning with Distributed Integrated Sensing and Communicationcited by 11
4 more
- 2023Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approachcited by 9
- 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
- 2023Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunitiescited by 4
- 2024Leverage Variational Graph Representation For Model Poisoning on Federated Learningcited by 3
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