Autor:
Erakogu

2nd Summer School on MegaData: Federated Machine Learning

On-site in Tartu 28 July - 10 August 2024
2nd Summer School on Federated Machine Learning

 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.

 

Application deadline 31 May 


Apply now

 

NB! Please note that every applicant must pay the application fee of 25 EUR. In the application form you must upload proof of payment. Please complete the payment on the application fee payment page. 

 

 

Focus area:Designing and Implementing Federated Machine LearningCoordinating unit at UTInstitute of Computer Science (Data Systems Group)
Study Field:Computer ScienceCourse LeaderFeras Awaysheh
FormatHands-on workshopLocationTartu, Estonia, Delta Centre
    
Course dates:28 July - 10 August 2024Apply by:31 May 2024
ECTS:3 (+2 for additional assignment)Fee:800 EUR
StudyMSc/PhDLanguageEnglish

Lecturers:

  • Feras Awaysheh, University of Tartu, Estonia
  • Sadi AlAwadi, Halmstad University, Sweden

Guest Talks:

  • Afsana Khan, Maastricht University, Netherlands.  
  • Daniel J. Beutel, Cambridge University, UK  
  • Florian van Daalen, University Maastricht, Netherlands 
  • Mohamed Elmahallawy, Missouri University of Science and Technology, USA 
  • William Lindskog, Technical University of Munich, Germany 
  • Salman Toor, Uppsala University, Sweden  
  • Hossam Fakhory, Petra University, Jordan  
  • Hassan Eldeeb, Tartu University, Estonia  

 

Last years participants' publications

 

 

Two weeks prior to the start of the programme an information file will be sent to all participants. This file contains the daily schedule and relevant contact information of the programme managers.

Students are responsible for their travel, accommodation and travel insurance (visa arrangements if needed) from their home country to Tartu and back to their home country. It is recommended to visit the Tartu Welcome Centre website and arrival and housing section to find accommodation opportunities.

 

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megadata

Mehreen Tahir, a final year PhD student at Dublin City University, Ireland participated in the UniTartu Summer School in 2023.

 

 

“My journey to the UniTartu Summer School was a bit of a happy accident. There are not many courses offered in Federated Learning. Thinking that I would at least get a chance to network with like-minded people from my field, I decided to give it a shot. And it turned out to be a beautiful and welcoming place, much more than what I could have imagined!” 

 

 

Read the article

 

MegaData: Federated Machine Learning in 2023