MegaData: Federated Machine Learning

This course provides an introduction to Federated Machine Learning (FL), a privacy-preserving distributed ML. The course will cover the foundational aspects of FL operation and deployment models in Edge computing. Modern FL technologies will cover various aspects, including different data distributions, aggregation algorithms, and communication efficiency approaches. The students will be introduced to state-of-the-art FL technologies and architectures and guided to investigate novel ideas in the area via lectures, practice sessions, and projects. We will also look at industry trends and discuss some innovations that have recently been developed.

The course targets MSc degree students and Ph.D. candidates looking to develop their capacity in modern computer deployment architecture at the Edge/Fog to meet the increasing demand in industry and academia. Also, the course is designed for students of joint data science and distributed system curriculum towards Edge Intelligence. We combine theory, practice sessions, and project assignments to learn about FL. After completing this course, you will learn more about designing and developing an FL solution. Some course material will be drawn from research papers, industry white papers, and technical reports.

The course can be taken on-site in Tartu, Estonia. We have a lecture and discussions in the morning session. Afternoon sessions are dedicated to practicing sessions and project work.

Course leader

Feras Awaysheh, University of Tartu

Target group

MSc/PhD

Course aim

On successfully completing this course, students should be able to:

1. Demonstrate knowledge of the emerging federated machine learning (FL) deployment architecture and requirements.
2. Understand the various capabilities of advanced FL solutions and develop the ability to choose adequate systems for different problems.
3. Apply state-of-the-art FL systems to build scalable solutions for various data privacy challenges in different application domains.
4. Apply qualitative and quantitative techniques in distributed machine intelligence through lectures and design projects using leading research trends to identify the strengths and weaknesses of the various systems.
5. Develop strategic thinking and soft skills for industry and business success using cutting-edge

Credits info

3 EC
ECTS: 3 (+2 for additional assignment)

Fee info

EUR 800: Course fee
EUR 227: Accommodation in the student dormitories for 13 nights.

Scholarships

Find out about the scholarships on our website.

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