An Interpretable Client Decision Tree Aggregation Process for Federated Learning to Improve Model Performance and Maintain Interpretability

The proposed Interpretable Client Decision Tree Aggregator For Federated Learning (ICDTA4FL) process aggregates multiple client decision trees into a global interpretable decision tree model, improving performance over local models while maintaining the inherent interpretability of decision trees.