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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Designing and Implementing an Advanced Algorithm to Measure the Trustworthiness Level of Federated Learning Models |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Artificial intelligence (AI) has immersed our daily lives and assists in the decision process of critical sectors such as medicine and law. Therefore it is now more important than ever before that AI systems developed are reliable, ethical, and do not cause harm to humans. The High-Level Expert Group on AI (AI-HLEG) of the European Commission has laid the foundation by defining seven key requirements for trustworthy AI systems. To address concerns about privacy risks associated with centralized learning approaches federated learning (FL) has emerged as a promising and widely used alternative. FL allows multiple clients to collaboratively train machine learning models without the need for sharing private data. Because of the high adaption of FL systems, ensuring that they are trustworthy is crucial. Previous research efforts have proposed a trustworthy FL taxonomy with six pillars, each comprehensively defined with notions and metrics. This taxonomy covers six of the seven requirements defined by the AI-HLEG. However, one notable aspect that has been largely overlooked by research is the requirement for environmental well-being in trustworthy AI/FL. This leaves a significant gap between the expectations set by governing bodies and the guidelines applied and measured by researchers. This master thesis addresses this gap by introducing the sustainability pillar to the trustworthy FL taxonomy and thus presenting the first taxonomy that comprehensively addresses all the requirements defined by the AI-HLEG. The sustainability pillar focuses on assessing the environmental impact of FL systems and incorporates three main aspects: hardware efficiency, federation complexity, and the carbon intensity of the energy grid, each with well-defined metrics. As a second contribution, this master thesis extends an existing prototype to evaluate the trustworthiness of FL systems with the sustainability pillar. The prototype is then extensively evaluated in various scenarios, involving different federation configurations. The results shed light on the trustworthiness of different federation configurations in different settings with varying complexities, hardware, and energy grids used. Importantly, the sustainability pillar’s score corrects the overall trust score by considering the environmental impact of FL systems across seven key pillars. Thus, the proposed taxonomy and prototype are the first to comprehensively address all seven AI-HLEG requirements and lay the foundation for a more accurate trustworthiness assessment of FL systems. |
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