Federated Learning (FL) is a method in Machine Learning (ML) where several devices train a model together without moving their raw data to a central place. This way of training is useful when information stays on local machines, especially in systems like edge networks, where data is collected and processed close to where it is generated. In many real-world environments, sending all the data to a central server is not possible. This might be due to privacy rules, slow network speeds, or the large size of the data. FL solves this problem by allowing each device to train a model with its data. Once the local training is done, the device only sends the updated model—usually in the form of numerical parameters—to a central server. This central server then gathers updates from all devices and builds a global model. The benefit of this setup is that private data stays on the device. At the same time, all devices contribute to building a shared model that learns from the experiences of the entire network. While this method supports privacy and can work even with limited connectivity, putting FL into action in edge networks can be difficult. Some of the common challenges include slow communication between devices, hardware with limited memory or battery, software differences across devices, and security risks related to model updates. To protect sensitive information, additional techniques can be used. Differential privacy, for example, adds random noise to the model updates to stop someone from guessing individual data points. Secure model sharing methods, such as encryption or masking, can also reduce the risk of exposing private data and ensure that model updates are real and have not been altered. The arrival of faster wireless communication, like 5G and future 6G, can help FL systems run more smoothly by improving the speed and reliability of connections between devices. FL brings new ways to solve problems in privacy-sensitive and data-rich environments. It works well when systems need to act quickly and can’t wait for data to be processed at a central location. This chapter gives a complete view of how FL works, how it can be applied to edge networks, what problems it faces, and what future directions it might take. It can be a starting point for those who want to build smart, secure, and responsive applications in distributed settings.