Multiple Classification with Split Learning

Understand

Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields.

• To solve this problem, we present privacy-preserving distributed deep learning method that allow clients to learn a variety of data without direct exposure.
• We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning.
• First, the common extractor, which is used by local clients, extracts secure features from the input data.