DSC-Workshop "Federated Learning (FL)"

© PixabayMotivbild.

In federated learning, (possibly sensitive) data remains in the local, decentralized repository (hospital, smartphone, etc), where it is processed locally by an ML algorithm. The results from different sources are then transformed into an overall model.
We present the method of FL, approaches to securing and processing personal data, and demonstrate its application by example. We encourage participants to actively participate in the panel discussion and to discuss possible application scenarios of their own.

09:00 – 09:15  Welcome

  Prof. Dr. Dirk Nowotka, Digital Science Center
  Christian-Albrechts-Universität zu Kiel

09:15 – 09:50  Federated Learning: Overview and  Introduction

  Prof. Dr. Olaf Landsiedel, Distributed Systems Christian-Albrechts-Universität zu Kiel

09:50 – 10:25  Insecure and Secure Distributed Learning

  Prof. Dr. Esfandiar Mohammadi, Institute for IT Security Universität zu Lübeck

10:25 – 10:35  Short Break

10:35 – 11:10  AI exchange – a federated learning use case across the  Atlantic

  Niklas Koser, Section Biomedical Imaging, Dept. of Radiology and Neuroradiology, (UKSH-Campus Kiel), Intelligent Imaging Lab (I2Lab)
  Christian-Albrechts-Universität zu Kiel

11:15 – 12:00  Panel  Discussion


When:  05.10.2023    09:00-12:00

Where:  CAP2 – Lecture Hall A

Contact and register:  hpfuhl@email.uni-kiel.de