Federated learning

This course teaches theoretical foundations and efficient algorithms for federated learning (FL) applications.
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Schedule:

Teaching time:

Self-paced

Location:

Online

Topic:

Information and communications technology

Form of learning:

Online

Provider:

Aalto University, FITech

Level:

Advanced

Credits:

5 By Aalto University (ECTS)

Fee:

Free of charge

Application period:

7.11.2022 – 19.2.2023

Target group and prerequisites

Familiarity with the concept of sequences and their limit & gradient and its use as a local linear approximation of functions. Knowledge about eigenvalue decomposition of positive semi-definite matrices. Suitable for students with advanced master level in machine learning or similar fields.

Course description

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.

Course contents

  • Multi-task learning
  • Complex networks
  • Clustering
  • Privacy-preserving machine learning
  • Large-scale machine learning
  • Python (Flask, scikit-learn)

Learning outcomes

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.

Course material

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.

Completion methods

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)
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