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HTTPS: Heterogeneous Transfer learning for spliT Prediction System evaluated on healthcare data☆
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DOI：10.1016/j.inffus.2024.102617.png)
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Abstract
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Internet of Medical Things (IoMT) facilitate revolutionary development in healthcare services, recognized as smart healthcare. By collecting big healthcare data and utilizing artificial intelligence algorithms, P4-medicine can be realized in intelligent diagnosis, risk analysis, and health management. As more data is collected, privacy and security become imperatives in healthcare research, and split learning is ideal for big data predictions, but relative research is at an early stage without systematically designing and considering data characteristics. In this paper, a Heterogeneous Transfer learning for spliT Prediction System (HTTPS) is proposed. HTTPS converts the dataset into both sparse and dense feature matrices, subsequently directing them into the sparse and dense embedding networks. For privacy considerations, embedding networks are designed as split learning to embed local features, and can further transfer experience from heterogeneous data. Experimental findings demonstrate that HTTPS outperforms the benchmark systems and has strong transferability. Furthermore, the designed mechanism motivates users to share some personal information to obtain precise predictions, and still provides a general model for privacy-conscious users.
Keywords:
Heterogeneity
Transfer learning
Heterogeneous transfer learning
Split learning
Healthcare
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