  In this work, we introduce SplitNN-driven Vertical Partitioning, a
configuration of a distributed deep learning method called SplitNN to
facilitate learning from vertically distributed features. SplitNN does not
share raw data or model details with collaborating institutions. The proposed
configuration allows training among institutions holding diverse sources of
data without the need of complex encryption algorithms or secure computation
protocols. We evaluate several configurations to merge the outputs of the split
models, and compare performance and resource efficiency. The method is flexible
and allows many different configurations to tackle the specific challenges
posed by vertically split datasets.
