Master's Thesis Research Data: Integrating Explainability into Federated Learning: A Non-functional Requirement Perspective

This data set contains the research data for the master's thesis: Integrating Explainability into Federated Learning: A Non-functional Requirement Perspective. The master's thesis was written by Nicolas Sebastian Schuler at the Computer Science Department at Karlsruhe Institute for Technology (KIT) in Germany. The data set contains: - Associate Jupyter notebooks for reproducing the figures in the master's thesis. - Generated experiment data by the federated learning simulations. - Results of the user survey conducted for the master's thesis. - Used Python Libraries. It also includes the submitted final thesis. Notice: The research data is split into multiple chunks and can be combined via the following command after downloading: $ cat thesis-results-part-* > thesis-results.tar.zst and extracted via: $ tar --zstd -xvf thesis-results.tar.zst