Today, the introduction of the Internet of Everything (IoE) which is marked by the interconnectivity of numerous gadgets and devices will result to the creation of a large amount of data. The decentralised approach of the federated learning expands to address these data by using them while circumventing the issues of privacy and security. The chapter illustrates the relationship between federated learning and IoE and also discusses the problems it faces. The chapter further discusses and elucidate the benefits of federated learning in the context of IoE while highlighting decentralized data ownership, edge computing, reinforced privacy preservation methods, and the aspect of cooperation in FL. Various applications that are relevant to real life in medical field, smart cities, industrial applications or IoT, and self-driven vehicles give a clear meaning to the impact of federated learning. In addition, the chapter discusses the latest trends and further research prospective for development of federated learning in context of IoE environments. Overall, the chapter is a synthesis of federated learning as a key facet of the IoE combining the benefits and difficulties of the technology innovation.