This is a PhD level course in a new branch of machine learning: federated learning. In federated learning, machine learning models are trained on mobile devices with an explicit effort to preserve the privacy of users’ data. Federated Learning combines areas such as supervised machine earning learning, privacy, distributed and edge computing, optimization, communication compression and systems. This is a new and fast-growing field with few theoretical results, and early production systems (e.g., Tensor Flow Federated). The aim of this course is the become familiar with the key results and practices of this field. As there is no textbook on this topic, the course material will be based on recent papers.