Textbook in PDF format

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.
It has been considered one of the key missing components in the existing 5G network and is widely recognized as one of the most sought-after functions for next-generation 6G communication systems. Nowadays, there are more than 10 billion Internet-of-Things (IoT) equipment and 5 billion smartphones that are equipped with artificial intelligence (AI)-empowered computing modules such as AI chips and GPU. On the one hand, the user equipment (UE) can be potentially deployed as computing nodes to process certain emerging service tasks such as crowdsensing tasks and collaborative tasks, which paves the way for applying AI in edge networks. On the other hand, in the paradigm of Machine Learning (ML), the powerful computing capability on these UEs can decouple ML from acquiring, storing, and training data in data centers as conventional methods.
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. As a result, the implementation of FL at edge networks can also decrease the costs of transmitting raw data, relieve the burden on backbone networks, reduce the latency for real-time decisions.
This book would explore recent advances in the theory and practice of FL, especially when it is applied to wireless communication systems. In detail, the book covers the following aspects:
1) principles and fundamentals of FL;
2) performance analysis of FL in wireless communication systems;
3) how future wireless networks (say 6G networks) enable FL as well as how FL frameworks/algorithms can be optimized when applying to wireless networks (6G);
4) FL applications to vertical industries and some typical communication scenarios.
Readers will explore a wide range of topics that show how Federated Learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find:
A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL
Comprehensive explorations of wireless communication network design and optimization for federated learning
Practical discussions of novel federated learning algorithms and frameworks for future wireless networks
Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution
Perfect for electrical and Computer Science engineers, researchers, professors, and postgraduate students with an interest in Machine Learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of Artificial Intelligence