  Federated Learning (FL) is known to perform Machine Learning tasks in a
distributed manner. Over the years, this has become an emerging technology
especially with various data protection and privacy policies being imposed FL
allows performing machine learning tasks whilst adhering to these challenges.
As with the emerging of any new technology, there are going to be challenges
and benefits. A challenge that exists in FL is the communication costs, as FL
takes place in a distributed environment where devices connected over the
network have to constantly share their updates this can create a communication
bottleneck. In this paper, we present a survey of the research that is
performed to overcome the communication constraints in an FL setting.
