  Due to their great performance and scalability properties neural networks
have become ubiquitous building blocks of many applications. With the rise of
mobile and IoT, these models now are also being increasingly applied in
distributed settings, where the owners of the data are separated by limited
communication channels and privacy constraints. To address the challenges of
these distributed environments, a wide range of training and evaluation schemes
have been developed, which require the communication of neural network
parametrizations. These novel approaches, which bring the "intelligence to the
data" have many advantages over traditional cloud solutions such as
privacy-preservation, increased security and device autonomy, communication
efficiency and high training speed. This paper gives an overview over the
recent advancements and challenges in this new field of research at the
intersection of machine learning and communications.
