  In the era of advanced technologies, mobile devices are equipped with
computing and sensing capabilities that gather excessive amounts of data. These
amounts of data are suitable for training different learning models. Cooperated
with advancements in Deep Learning (DL), these learning models empower numerous
useful applications, e.g., image processing, speech recognition, healthcare,
vehicular network and many more. Traditionally, Machine Learning (ML)
approaches require data to be centralised in cloud-based data-centres. However,
this data is often large in quantity and privacy-sensitive which prevents
logging into these data-centres for training the learning models. In turn, this
results in critical issues of high latency and communication inefficiency.
Recently, in light of new privacy legislations in many countries, the concept
of Federated Learning (FL) has been introduced. In FL, mobile users are
empowered to learn a global model by aggregating their local models, without
sharing the privacy-sensitive data. Usually, these mobile users have slow
network connections to the data-centre where the global model is maintained.
Moreover, in a complex and large scale network, heterogeneous devices that have
various energy constraints are involved. This raises the challenge of
communication cost when implementing FL at large scale. To this end, in this
research, we begin with the fundamentals of FL, and then, we highlight the
recent FL algorithms and evaluate their communication efficiency with detailed
comparisons. Furthermore, we propose a set of solutions to alleviate the
existing FL problems both from communication perspective and privacy
perspective.
