Max amount of FITech students: 50
Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.
This course teaches theoretical foundations and efficient algorithms for federated learning (FL) applications. FL is an umbrella term for training machine learning models in a collaborative fashion from distributed collections of data. These collections can be modelled as a graph whose nodes represent computational units (such as a smartphone). FL techniques are privacy-friendly as they do not require to share raw data between nodes but only model parameter updates. You will learn how to formulate FL applications as a regularised empirical risk minimisation and solve it using distributed implementations of gradient descent.
- Multi-task learning
- Complex networks
- Privacy-preserving machine learning
- Large-scale machine learning
- Python (Flask, scikit-learn)
After successfully completing this course, the student
- can model networked data and models using concepts from graph theory.
- can formulate FL problems as optimisation problems.
- is familiar with distributed optimisation methods (gradient-descent, primal-dual).
- can implement FL methods in Python.
We provide a web server for Python programming (JupyterHub). Students only need a computer with a web browser running that is connected to the internet.
Course book: A. Jung, “Machine Learning: The Basics”, Springer, Singapore, 2022.
Independent study, assignments, project-work and peer-grading.
Students can collect points from different activities:
- programming assignments
- theory questions (quizzes)
- student project
- oral exam (via Zoom)
More information in the Aalto University study guide.
You can get a digital badge after completing this course.
personalization; privacy protection; distributed learning; networks, machine learning ML koneoppiminen, tietosuoja, yksityisyyden suoja hajautettu oppiminen
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Tekoäly ja koneoppiminen,