MegaData. Federated Machine Learning, Short Course | University of Tartu | Tartu, Estonia
Studyportals
Short On Campus

MegaData. Federated Machine Learning

14 days
Duration
800 EUR/full
800 EUR/full
Unknown
Tuition fee
Unknown
Apply date
Unknown
Start date

About

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.

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

Language

English

Credits

3 ECTS

+2 ECTS for additional assignment

Delivered

On Campus

Academic 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

To always see correct tuition fees
  • International

    800 EUR/full
    Tuition Fee
    Based on the tuition of 800 EUR for the full programme during 14 days.
  • EU/EEA

    800 EUR/full
    Tuition Fee
    Based on the tuition of 800 EUR for the full programme during 14 days.

Living costs for Tartu

300 - 600 EUR /month
Living costs

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

Other interesting programmes for you

Our partners

MegaData. Federated Machine Learning
University of Tartu
MegaData. Federated Machine Learning
-
University of Tartu

Wishlist

Go to your profile page to get personalised recommendations!