Making Split Learning Resilient to Label Leakage by Potential Energy Loss. (arXiv:2210.09617v1 [cs.CR])

As a practical privacy-preserving learning method, split learning has drawn
much attention in academia and industry. However, its security is constantly
being questioned since the intermediate results are shared during training and
inference. In this paper, we focus on the privacy leakage problem caused by the
trained split model, i.e., the attacker can use a few labeled samples to
fine-tune the bottom model, and gets quite good performance. To prevent such
kind of privacy leakage, we propose the potential energy …

energy loss making split learning