Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

16 Mar 2020Sharif AbuadbbaKyuyeon KimMinki KimChandra ThapaSeyit A. CamtepeYansong GaoHyoungshick KimSurya Nepal

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet