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Task-Driven Transferred Vertical Federated Deep Learning for Multivariate Internet of Things Time-Series Analysis.
- Source :
- Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 11, p4606, 38p
- Publication Year :
- 2024
-
Abstract
- As big data technologies for IoT services develop, cross-service distributed learning techniques of multivariate deep learning models on IoT time-series data collected from various sources are becoming important. Vertical federated deep learning (VFDL) is used for cross-service distributed learning for multivariate IoT time-series deep learning models. Existing VFDL methods with reasonable performance require a large communication amount. On the other hand, existing communication-efficient VFDL methods have relatively low performance. We propose TT-VFDL-SIM, which can achieve improved performance over centralized training or existing VFDL methods in a communication-efficient manner. TT-VFDL-SIM derives partial tasks from the target task and applies transfer learning to them. In our task-driven transfer approach for the design of TT-VFDL-SIM, the SIM Partial Training mechanism contributes to performance improvement by introducing similar feature spaces in various ways. TT-VFDL-SIM was more communication-efficient than existing VFDL methods and achieved an average of 0.00153 improved MSE and 7.98% improved accuracy than centralized training or existing VFDL methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- FEDERATED learning
TIME series analysis
INTERNET of things
BIG data
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 14
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 177852918
- Full Text :
- https://doi.org/10.3390/app14114606