  As more and more companies store their customers' data; various information
of a person is distributed among numerous companies' databases. Different
industrial sectors carry distinct features about the same customers. Also,
different companies within the same industrial sector carry similar kinds of
data about the customers with different data representations. Cooperation
between companies from different industrial sectors, called vertical
cooperation, and between the companies within the same sector, called
horizontal cooperation, can lead to more accurate machine learning models and
better estimations in tasks such as credit scoring. However, data privacy
regulations and compatibility issues for different data representations are
huge obstacles to cooperative model training. By proposing the training
framework MICS and experimentation on several numerical data sets, we showed
that companies would have an incentive to cooperate with other companies from
their sector and with other industrial sectors to jointly train more robust and
accurate global models without explicitly sharing their customers' private
data.
