This master-level course is offered in Spring 2025 at Aalto University
Enrollment Options:
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Finnish University (or University of Applied Sciences) Students
Please contact your study administrator for enrollment details. -
Open Access
Anyone interested in following the course without formal enrollment can subscribe to the course mailing list.
see the course site for more info.
Federated Learning (FL) is a decentralized approach to training machine learning models, designed to retain local data privacy by training models without centralizing datasets. This course covers the fundamental linear algebra and calculus needed to analyze and design FL systems, focusing on real-world applications like weather forecasting and healthcare.
Participants will learn:
- to formulate FL applications as optimization problems
- to design FL algorithms using distributed optimization
- about key requirements for trustworthy AI
- to critically evaluate the trustworthiness of FL systems
- A. Jung, "Machine Learning: The Basics," Springer, Singapore, 2022. Available via Aalto Library: here. Preprint.
- A. Jung, "Federated Learning: From Theory to Practice," Aalto University, 2024. click me
This material is provided for educational and research purposes. Free use and redistribution are permitted with appropriate attribution. Please credit CS-E4740 - Federated Learning, Aalto University in any shared or derived works.
Preferred Citation:
A. Jung, "Federated Learning: From Theory to Practice," Aalto University, 2022.