  Machine learning (ML) has been pervasively researched nowadays and it has
been applied in many aspects of real life. Nevertheless, issues of model and
data still accompany the development of ML. For instance, training of
traditional ML models is limited to the access of data sets, which are
generally proprietary; published ML models may soon be out of date without an
update of new data and continuous training; malicious data contributors may
upload wrongly labeled data that leads to undesirable training results; and the
abuse of private data and data leakage also exit. With the utilization of
blockchain, an emerging and swiftly developing technology, these problems can
be efficiently solved. In this paper, we survey the convergence of
collaborative ML and blockchain. Different ways of the combination of these two
technologies are investigated and their fields of application are examined.
Discussion on the limitations of current research and their future directions
are also included.
