• clustering
• data privacy
• decision trees
• federated learning
• personalization

This thesis proposes a reliable approach for clustering participants in a federated learning environment, specifically using decision trees as the learning model to ensure explainability in classification tasks. In Federated Learning, participants train models locally on their data without sharing raw information, yet discrepancies in data distribution pose challenges in constructing an effective global model. Grouping participants based on similar decision patterns can enhance performance by enabling models to be trained on more homogeneous data.
To achieve this, three different measures for assessing the distance between decision trees were tested on the N-BaIoT dataset. The prediction similarity metric evaluates tree distances based on differences in their predictions, while the WSV measures structural differences between trees. Additionally, the cosine distance was used to assess distances based on embeddings extracted from the trees generated using a VAE. These distance metrics were then applied to cluster participants using hierarchical clustering.
Furthermore, the three metrics were also compared as methods for selecting a representative tree, both from the entire federation and within individual clusters. This approach contrasts with traditional aggregation methods, exploring whether selecting a single representative tree could serve as a viable alternative to constructing a fully aggregated model.