  Split learning (SL) aims to protect user data privacy by distributing deep
models between client-server and keeping private data locally. In SL training
with multiple clients, the local model weights are shared among the clients for
local model update. This paper first reveals data privacy leakage exacerbated
from local weight sharing among the clients in SL through model inversion
attacks. Then, to reduce the data privacy leakage issue, we propose and analyze
privacy-enhanced SL (P-SL) (or SL without local weight sharing). We further
propose parallelized P-SL to expedite the training process by duplicating
multiple server-side model instances without compromising accuracy. Finally, we
explore P-SL with late participating clients and devise a server-side
cache-based training method to address the forgetting phenomenon in SL when
late clients join. Experimental results demonstrate that P-SL helps reduce up
to 50% of client-side data leakage, which essentially achieves a better
privacy-accuracy trade-off than the current trend by using differential privacy
mechanisms. Moreover, P-SL and its cache-based version achieve comparable
accuracy to baseline SL under various data distributions, while cost less
computation and communication. Additionally, caching-based training in P-SL
mitigates the negative effect of forgetting, stabilizes the learning, and
enables practical and low-complexity training in a dynamic environment with
late-arriving clients.
