DOI: 10.7910/dvn/pnmarj
title: Master's Thesis Research Data: Integrating Explainability into Federated Learning: A Non-functional Requirement Perspective

description: 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-* &gt; thesis-results.tar.zst
and extracted via:
$ tar --zstd -xvf thesis-results.tar.zst
description: Abstract:

In recent years, the advancements of Artificial Intelligence (AI)-driven software systems have reached a seemingly unstoppable momentum. From simple applications like to-do-
applications to the most complex ones like banking applications, the demand to integrate AI into applications is exceptionally high nowadays. Whilst researchers and practitioners could gain much understanding in developing AI applications, one critical quality attribute seems left behind. This can be emphasized in a simple analogy: although we usually demand professors and teachers to be qualified for their jobs and to be able to explain their reasoning to students, for AI, our bar seems much lower if not absent. Some scholars see this lack of explainability and resulting intransparency as detrimental. Despite much research efforts in Explainable Artificial Intelligence (XAI), a meaningful integration thereof is still very much a subject of ongoing research. Furthermore, for even the most critical applications, e.g., the medical field, traditional Machine Learning (ML) methods cannot satisfy strict privacy requirements. For this reason, the AI paradigm Federated Learning (FL) emerged as a means to train ML models decentralized. Despite the benefits of explainability and FL being present today, the integration of explainability into FL is still seriously lacking. 

In this master’s thesis, we empirically researched the interaction between FL and explainability. We show that the global FL model outperforms the local one in terms of accuracy and prevalent XAI metrics and that data distribution affects the outcome more than the employed FL algorithm. We investigate which XAI method is the most stable concerning the effect of the predictive multiplicity, propose an adapted version of the XAI method proposed in [50] to optimize explanations for XAI metrics, and lastly also show that further security-related aspects influence XAI methods in a non-negligible way. Furthermore, we will present a concept of explainability derived from a literature search encompassing ideas from multiple disciplines with abduction as the central element in an attempt to better understand what the term explainability means from a human-centric point of view. Finally, with our user survey, we show that while demand for explainability is considered very high, there exists a gap between the usage of AI and XAI methods and a notable difference in how explanations are deemed satisfactory in the first place. Moreover, we show that the benefit of XAI metrics as a means to improve explanations is questionable.