Event Date: February 10, 2022 17:45
Trustworthy Federated Learning
Abstract - Data science is taking the world by storm, but its confident application in practice requires that the methods used are effective and trustworthy. This was already a difficult task when data fit onto a desktop computer, but becomes even harder now that data sources are ubiquitous and inherently distributed. In many applications (such as autonomous driving, industrial machines, or healthcare) it is impossible or hugely impractical to gather all their data into one place, not only because of the sheer size but also because of data privacy. Federated learning offers a solution: models are trained only locally and combined to create a well-performing joint model - without sharing data. However, this comes at a cost: unlike classical parallelizations, the result of federated learning is not the same as centralized learning on all data. To make this approach trustworthy, we need to guarantee high model quality (as well as robustness to adversarial examples); this is challenging in particular for deep learning where satisfying guarantees cannot even be given for the centralized case. Simultaneously ensuring data privacy and maintaining effective and communication-efficient training is a huge undertaking. In my talk I will present practically useful and theoretically sound federated learning approaches, and show novel approaches to tackle the exciting open problems on the path to trustworthy federated learning.
Biographie - I am leader of the research group "Trustworthy Machine Learning" at the Institut für KI in der Medizin (IKIM), located at the Ruhr-University Bochum. In 2021 I was a postdoctoral researcher at the CISPA Helmholtz Center for Information Security in the Exploratory Data Analysis group of Jilles Vreeken. From 2019 to 2021 I was a postdoctoral research fellow at Monash University, where I am still an affiliated researcher. From 2011 to 2019 I was a data scientist at Fraunhofer IAIS, where I lead Fraunhofer’s part in the EU project DiSIEM, managing a small research team. Moreover, I was a project-specific consultant and researcher, e.g., for Volkswagen, DHL, and Hussel, and I designed and gave industrial trainings. Since 2014 I was simultaneously a doctoral researcher at the University of Bonn, teaching graduate labs and seminars, and supervising Master’s and Bachelor’s theses. Before that, I worked for 10 years as a software developer.
