Introduction to Federated Learning

Discover the future of collaborative AI - without sharing sensitive data. This webinar offers an accessible introduction to federated learning, designed for beginners with no prior experience. We’ll break down the core concepts and explain why federated learning is transforming how organizations work with data. 

Curious how organizations can build powerful AI models without ever centralizing their data? Join our webinar and step into the world of federated learning - one of the most exciting and rapidly evolving approaches in modern machine learning. This session is designed specifically for newcomers. We start from the very basics and guide you through the full journey: from understanding the limitations of traditional centralized learning to discovering how federated learning enables collaboration across devices and organizations while preserving privacy and data ownership.

You’ll learn:

• Why federated learning matters now in an era of strict data protection and distributed data sources
• How it works under the hood, including model training, aggregation, and communication between participants
• Key challenges and trade-offs, such as data heterogeneity, communication efficiency, and security considerations
• Real-world applications, from healthcare and finance to mobile devices and edge computing
• Where the field is heading, and how you can start exploring it yourself

Whether you're a data scientist, engineer, decision-maker, or simply curious about AI innovations, this webinar will give you a solid foundation and practical intuition for a technique that is shaping the future of machine learning. Don’t miss this opportunity to demystify federated learning and discover how you can leverage it in your own projects or organization.

Target audience

The webinar is aimed at everyone interested in understanding and/or applying Federated Learning.

This webinar is open and free of charge for all participants from academia, industry, and public administration from EU and/or EuroHPC JU member countries.

Entry level & prerequisites

Beginner - no prior experience required.

Agenda

• Welcome & Presentation of AI Factory Austria AI:AT
• Introduction and fundamentals of Federated Learning
• The Federated Learning workflow
• Example use cases and frameworks
• Questions & Discussion
   

Speakers

Sarah Reisenbauer (AIT Austrian Institute of Technology)      
Sarah Reisenbauer works in the areas of data analytics and machine learning at the Austrian Institute of Technology (AIT), center for energy. She has absolved her bachelor and master studies in technical physics at the Vienna University of Technology. In the following doctoral studies, she conducted experiments in the fields of quantum optics and quantum information. Since 2021 she has been working as a research engineer and project coordinator at AIT with a focus on electric power system digitalization by means of artificial intelligence. Main activities of her work are in the areas of data analytics, the determination of the state of the electric distribution grid and forecasting of electric energy generation and loads. 
 

Registration

Registration is required, the link to the registration form can be found on the top of this page in the menu on the left. Please register with your official institutional email address to prove your affiliation.

You will get the Zoom link in the automatic confirmation by email (subject starting with "[Indico] Registration"), please check your Spam/Junk folders). We will also send 1–2 reminders before the webinar.

Please do not hesitate to contact us at training@ai-at.eu if you have any questions.

Organizers

This webinar is organized by AI Factory Austria AI:AT.

Acknowledgements

AI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101253078. The JU receives support from the Horizon Europe Programm of the European Union and Austria (BMIMI / FFG).