The confluence of 5G networks and the Internet of Things (IoT) promises transformative capabilities for real-time data processing and decision-making. However, this advancement also brings forth significant concerns regarding data security and privacy. This research paper explores the application of Federated Learning (FL) at the edge of 5G networks as a solution to address these challenges. FL, a decentralized machine learning paradigm, facilitates collaborative model training across IoT devices while maintaining data on the edge. This approach not only safeguards user privacy but also minimizes the necessity for extensive data transfer to centralized servers, thereby optimizing bandwidth and reducing latency. In this paper, we delve into the foundational principles of FL, assess its suitability in edge computing environments, and present case studies illustrating its effectiveness across various IoT scenarios. Furthermore, we examine the essential security considerations, potential enhancements, and deployment strategies necessary to fully realize the potential of FL in 5G-powered IoT ecosystems. This research contributes to the development of secure, privacy-preserving, and efficient IoT solutions in the 5G era.