A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning. (arXiv:2309.08697v1 [cs.CR])

Abstract: 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 (AMs) and then sends them to the server

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