  With the help of a new architecture called Edge/Fog (E/F) computing, cloud
computing services can now be extended nearer to data generator devices. E/F
computing in combination with Deep Learning (DL) is a promisedtechnique that is
vastly applied in numerous fields. To train their models, data producers in
conventional DL architectures with E/F computing enable them to repeatedly
transmit and communicate data with third-party servers, like Edge/Fog or cloud
servers. Due to the extensive bandwidth needs, legal issues, and privacy risks,
this architecture is frequently impractical. Through a centralized server, the
models can be co-trained by FL through distributed clients, including cars,
hospitals, and mobile phones, while preserving data localization. As it
facilitates group learning and model optimization, FL can therefore be seen as
a motivating element in the E/F computing paradigm. Although FL applications in
E/F computing environments have been considered in previous studies, FL
execution and hurdles in the E/F computing framework have not been thoroughly
covered. In order to identify advanced solutions, this chapter will provide a
review of the application of FL in E/F computing systems. We think that by
doing this chapter, researchers will learn more about how E/F computing and FL
enable related concepts and technologies. Some case studies about the
implementation of federated learning in E/F computing are being investigated.
The open issues and future research directions are introduced.
