You can formally enrol this course as

• university (of applied science) student in Finland (contact your study administrator for details)
• adult learner in Finland via FiTech
• student at Chalmers, KTH (Sweden), NTNU (Norway), DTU (Denmark) via Registration for Externals

Lectures *** Lecture Notes *** Assignments *** FL Project

Many machine learning (ML) application domains, such as numerical weather prediction, generate decentralized collections of local datasets. A naive application of basic ML methods [1] would require collecting these local datasets at a central point. However, this approach might be unfavourable for several reasons, including inefficient use of computational infrastructure or the need for more privacy.

Federated learning (FL) aims to train ML models in a decentralized and collaborative fashion. FL methods require only the exchange of model parameter updates instead of raw data. These methods are appealing computationally and from a privacy protection perspective. Indeed, FL methods leverage distributed computational resources and minimize the leakage of private information irrelevant to the learning task.

This course teaches you how to apply concepts from linear algebra (arrays of numbers) and calculus (smooth curves) to analyze and design federated learning (FL) systems. You will learn to formulate "real-world" applications, ranging from high-precision weather forecasting to personalized health care, as optimization problems and solve them using distributed optimization algorithms. We offer the courses in a basic variant (5 credits) that you extend to an extended variant (10 credits) by completing a student project. This student project allows you to pilot (get feedback for) ideas for your thesis or current research.

To get a more concrete idea of what to expect, have a look at the draft for the lecture notes.

[1] A. Jung, "Machine Learning. The Basics," Springer, Singapore, 2022. available via Aalto library here. preprint.

• Federated learning: Basics and application to the mobile keyboard (ML Tech Talks)
• Network Flows in Federated Learning (slides for an IEEE Seminar Talk)