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
