Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy. (arXiv:2212.00250v1 [cs.CR])

Split learning (SL) aims to protect user data privacy by splitting deep
models between client-server and keeping private data locally. SL has been
demonstrated to achieve similar accuracy as the centralized learning model. In
SL with multiple clients, the local training weights are shared between clients
for local model aggregation. This paper investigates the potential of data
leakage due to local weight sharing among the clients in SL by performing model
inversion attacks. To mitigate the identified leakage issue, we …

client client-side data data privacy local privacy sharing split learning