Learning for the Internet of Things is a combination of advanced learning techniques for the Internet of Things (IoT) encompassing a range of cutting-edge approaches, including Deep Learning with CNNs, RNNs, and transformers, Federated Learning, edge AI for local data processing, reinforcement learning for autonomous decision-making, and their applications in real time. The book is structured into thirteen chapters; each comes with its own dedicated contributions and future research directions. Chapter 1 introduces IoT and the use of Edge computing, particularly cloud computing, and mobile edge computing. This chapter also mentions the use of edge computing in various real-time applications such as healthcare, manufacturing, agriculture, and transportation. Chapter 2 motivates mathematical modeling for Federated Learning systems with respect to IoT and its applications. Further Chapter 3 extends the discussion of Federated Learning for IoT, which has emerged as a privacy-preserving distributed Machine Learning approach.