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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Poisoning Attack Behavior Detection in Federated Learning |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Federated Learning has emerged as a viable alternative to traditional Machine Learning in scenarios, where client data is highly sensitive and the client devices are capable of local computation of model updates. Much of the recent work in the area of Federated Learning was targeted towards the centralized setting, where locally computed model updates are aggregated by a centralized server to create a global model. Recently, a new approach has emerged: a fully decentralized network where clients themselves are responsible for aggregating updates of neighboring nodes. Recent interest in this setting has spurred interest in the creation of a unified framework, that is capable of simulating both centralized, as well as decentralized Federated Learning. Ideally, this framework is also capable of simulation under a byzantine setting, where client devices may be malicious and attempt to harm the shared model by providing false data or poisoned model updates. This would allow for direct comparison of the centralized and decentralized approach under various scenarios, such as different attacks or aggregation rules. Such a comparison could deliver valuable insights on their behavioral differences and benefits/disadvantages of either setting. The FedStellar framework fills these requirements by providing capabilities to simulate either scenario with benign clients, yet so far it offers no support for the byzantine setting. In this thesis, the FedStellar framework is expanded with additional functionality, implementing more data- as well as model-based poisoning attacks and Aggregation Rules. Additionally, the new functionality is used to simulate and analyze different scenarios. |
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