An interpretable client decision tree aggregation process for federated learning Argente-Garrido, Alberto Zuheros, Cristina Luzón García, María Victoria Herrera Triguero, Francisco Federated Learning Decision Trees Interpretability Aggregation Process Data Privacy This research results from the Strategic Project IAFER-Cib (C074/23), as a result of the collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Granada. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation). Trustworthy Artificial Intelligence solutions are essential in today’s data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art. 2025-02-17T12:20:14Z 2025-02-17T12:20:14Z 2024-04-03 journal article Published version: Argente-Garrido, Alberto et al. Information Sciences Volume 694, March 2025, 121711. https://doi.org/10.1016/j.ins.2024.121711 https://hdl.handle.net/10481/102413 10.1016/j.ins.2024.121711 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier