An Interpretable Client Decision Tree Aggregation process for Federated Learning

arXiv:2404.02510v1 Announce Type: new
Abstract: 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 …

abstract aggregation applications artificial artificial intelligence arxiv client cs.ai cs.lg data data-driven decision decision trees distributed emergence explainability federated learning intelligence machine machine learning privacy process robustness safety solution solutions transparency tree trees trustworthy type