Due to the rapidly developing technologies such as vehicle applications, mobile devices, and the Internet of Things (IoT), an efficient architecture is required to maintain the server decentralized. Fog computing describes decentralized computing infrastructure that ensures easy communication maintain networks to store and manage data between edge devices and a server.
   Sustainable benefits of fog computing are enhanced speed, reduced latency, reliability and resiliency, and scalability. Fog computing requires distributed collaborative resource management conducive to communication and computation resource maintenance of edge devices and servers. Federated learning is the new paradigm that collaboratively trains the models by integrating a huge number of distributed user groups without exchanging data to elude the server from gathering user-sensitive data. Effective cooperation of computation resources in fog computing with data privacy preservation is achieved by utilizing federated learning.