Personnel status detection model suitable for vertical federated learning structure
Abstract
With the improvement of the medical system, the universal access of wearable devices and people's greater concern about personal health, personnel health detection has received greater attention. However, existing personnel status detection faces the problem that existing models cannot get more accurate results by multiple types of data because the data is difficult to obtain across domains due to privacy issue. In this paper, we propose a vertical federated learning model for person status detection using multimodal data. Considering the varying compute capability of training devices and the different inference speed caused by modality complexity, a lightweight feature extraction sub-model is used, while ensuring data privacy and tolerating accuracy degradation. Also, we design a Fast and Secure (FS) module that effectively reduces the amount of data onto the network transmission process, the model's dependence on the network and the risk of data being intercepted by the third party. Experiment results demonstrate the feasibility of multimodal vertical federated learning and the accuracy is increased by 5.6 percentage points compared with unimodal. The FS module reduces the amount of network transmission information by 50% and 48.1% during model use and model training, respectively
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A multi-modal machine learning model uses homomorphic encryption to securely share data required for training machine learning models.
“A multi-modal machine learning model is proposed to effectively solve the problem of data islands and uses homomorphic encryption to securely share data required for training machine learning models [36].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [36]
HE is very effective for FL privacy preservation because most operations required by FL systems, such as aggregation, model updates, and gradient calculations, can be performed without accessing raw data.
“This approach is very effective for FL privacy preservation as most of the operations required by the FL systems like aggregation, model updates, and gradient calculations can be performed without accessing the raw data [34,35,36,37,38].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [36]
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- Authors
- Ji, J., Yan, D., & Mu, Z.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Venue
- 2022 The 6th International Conference on Machine Learning and Soft Computing
- Publisher
- ACM
- Published
- 2022-01-15
- Pages
- 98-104
- OpenAlex
- W4224094724
- Cited by
- 12
- References
- 19
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 3 · 2022: 1
- FWCI
- 1.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Soft Computing (ICMLSC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- vertical federated learning, multimodal fusion, wearable devices, health monitoring, data privacy, lightweight feature extraction
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