Overview
On-site in Tartu 28 July - 10 August 2024
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 and doctoral students 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 practice sessions and project work.
Programme Structure
Tuesday, July 30
Introduction to Machine Learning (ML pipelines).
ML Lifecycle and centralized deep learning.
Wednesday, July 31
Data privacy and Data Protection Regulation (e.g., GDPR)
Introduction to Federated Machine learning
Thursday, 1 August
FL challengers of FLFL aggregation algorithms and applications
Horizontal and Vertical Data distribution
Friday, 2 August
Intro to FL open-source frameworks (e.g., FEDn and FLOWER)
Frameworks installation and configuration
Monday, 5 August
FL Architectures and Communication efficiency techniques
Use cases cross-silo and cross-device
Tuesday, 6 August
New trends in FL and 5.Personalized modeling
E.g., Meta Learning, Transfer Learning, Split Learning, and Interactive Learning.
Wednesday, 7 August
AutoML as a solution for FL optimization
Lightweight ML (e.g., Edge Impulse) and FL Security scenarios
Thursday, 8 August
Wrap up with real applications and FL for medical image analysis
Friday, 9 August
Participant's projects
Audience
MSc/PhD
Lecturers
- Feras Awaysheh, University of Tartu, Estonia
- Sadi AlAwadi, Halmstad University, Sweden
Key information
Duration
- Full-time
- 14 days
Start dates & application deadlines
- Starting
- Apply before
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Language
Credits
+2 ECTS for additional assignment
Delivered
Disciplines
Computer Sciences Artificial Intelligence Machine LearningAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
MSc/PhD
Entry requirements:
Interest in designing and developing privacy-preserving ML solutions. Also, the course is designed for joint data science and distributed system curriculum students. Good Machine Learning is a mandatory prerequisite. Students are encouraged (but not necessarily required) completed Computer Networks, Distributed Systems, Cloud Computing, and Big Data Management courses.
- Online application form
- Application fee of 25 EUR
- Motivation letter (up to 1 page) that demonstrates the applicant’s motivation to participate, his/her expectations about the program, how participation in the summer program relates to his/her studies and interests, and how the applicant plans to use the gained experience and knowledge in the future).
- Transcript of academic records
- Copy of passport
Tuition Fee
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International
800 EUR/fullTuition FeeBased on the tuition of 800 EUR for the full programme during 14 days. -
EU/EEA
800 EUR/fullTuition FeeBased on the tuition of 800 EUR for the full programme during 14 days.
Living costs for Tartu
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
1. Estonian National Scholarships at StudyinEstonia.ee.
You can read more about the scholarships on the homepage of StudyinEstonia.ee.
2. ENLIGHT scholarship
More information and the application form are on the ENLIGHT scholarship page: https://ut.ee/en/content/enlight-scholarship
3. DAAD scholarship
More information and application form on the DAAD scholarship page: https://ut.ee/en/content/daad-scholarship
4. Partial tuition fee coverage scholarship
More information and the application form for the partial tuition fee coverage are on the scholarship page: https://ut.ee/en/content/scholarships-summer-programmes