  Motivated by the advancing computational capacity of distributed end-user
equipments (UEs), as well as the increasing concerns about sharing private
data, there has been considerable recent interest in machine learning (ML) and
artificial intelligence (AI) that can be processed on on distributed UEs.
Specifically, in this paradigm, parts of an ML process are outsourced to
multiple distributed UEs, and then the processed ML information is aggregated
on a certain level at a central server, which turns a centralized ML process
into a distributed one, and brings about significant benefits. However, this
new distributed ML paradigm raises new risks of privacy and security issues. In
this paper, we provide a survey of the emerging security and privacy risks of
distributed ML from a unique perspective of information exchange levels, which
are defined according to the key steps of an ML process, i.e.: i) the level of
preprocessed data, ii) the level of learning models, iii) the level of
extracted knowledge and, iv) the level of intermediate results. We explore and
analyze the potential of threats for each information exchange level based on
an overview of the current state-of-the-art attack mechanisms, and then discuss
the possible defense methods against such threats. Finally, we complete the
survey by providing an outlook on the challenges and possible directions for
future research in this critical area.
