  In networks of independent entities that face similar predictive tasks,
transfer machine learning enables to re-use and improve neural nets using
distributed data sets without the exposure of raw data. As the number of data
sets in business networks grows and not every neural net transfer is
successful, indicators are needed for its impact on the target performance-its
transferability. We perform an empirical study on a unique real-world use case
comprised of sales data from six different restaurants. We train and transfer
neural nets across these restaurant sales data and measure their
transferability. Moreover, we calculate potential indicators for
transferability based on divergences of data, data projections and a novel
metric for neural net similarity. We obtain significant negative correlations
between the transferability and the tested indicators. Our findings allow to
choose the transfer path based on these indicators, which improves model
performance whilst simultaneously requiring fewer model transfers.
