  Nowadays, gathering high-quality training data from multiple data sources
with privacy preservation is a crucial challenge to training high-performance
machine learning models. The potential solutions could break the barriers among
isolated data corpus, and consequently enlarge the range of data available for
processing. To this end, both academic researchers and industrial vendors are
recently strongly motivated to propose two main-stream folders of solutions
mainly based on software constructions: 1) Secure Multi-party Learning (MPL for
short); and 2) Federated Learning (FL for short). The above two technical
folders have their advantages and limitations when we evaluate them according
to the following five criteria: security, efficiency, data distribution, the
accuracy of trained models, and application scenarios.
  Motivated to demonstrate the research progress and discuss the insights on
the future directions, we thoroughly investigate these protocols and frameworks
of both MPL and FL. At first, we define the problem of Training machine
learning Models over Multiple data sources with Privacy Preservation (TMMPP for
short). Then, we compare the recent studies of TMMPP from the aspects of the
technical routes, the number of parties supported, data partitioning, threat
model, and machine learning models supported, to show their advantages and
limitations. Next, we investigate and evaluate five popular FL platforms.
Finally, we discuss the potential directions to resolve the problem of TMMPP in
the future.
