Explore the concepts, algorithms, and applications underlying Federated Learning. In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering Federated Learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects Federated Learning performance, accuracy, convergence, scalability, and security and privacy. Federated Learning (FL) has been widely acknowledged as one of the most essential enablers to bring network edge intelligence into reality, as it can enable collaborative training of ML models while enhancing individual user privacy and data security. Empowered by the growing computing capabilities of UEs, FL trains ML models locally on each device where the raw data never leaves the device. Specifically, FL uses an iterative approach that requires a number of global iterations to achieve a global model accuracy. In each global iteration, UEs take a number of local iterations up to a local model accuracy. This book would explore recent advances in the theory and practice of FL, especially when it is applied to wireless communication systems.