Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption. (arXiv:2309.10517v1 [cs.CR])

Split learning (SL) is a new collaborative learning technique that allows
participants, e.g. a client and a server, to train machine learning models
without the client sharing raw data. In this setting, the client initially
applies its part of the machine learning model on the raw data to generate
activation maps and then sends them to the server to continue the training
process. Previous works in the field demonstrated that reconstructing
activation maps could result in privacy leakage of client …

client data encryption homomorphic encryption love machine machine learning machine learning models privacy server share sharing split learning train training