  Recently, there has been the development of Split Learning, a framework for
distributed computation where model components are split between the client and
server (Vepakomma et al., 2018b). As Split Learning scales to include many
different model components, there needs to be a method of matching client-side
model components with the best server-side model components. A solution to this
problem was introduced in the ExpertMatcher (Sharma et al., 2019) framework,
which uses autoencoders to match raw data to models. In this work, we propose
an extension of ExpertMatcher, where matching can be performed without the need
to share the client's raw data representation. The technique is applicable to
situations where there are local clients and centralized expert ML models, but
the sharing of raw data is constrained.
