Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy

2026-02-10

AI Summary

This work addresses the degradation of model interpretability caused by differential privacy in federated learning. To this end, we propose FEXT-DP, the first decision tree–based federated interpretable model that significantly enhances interpretability while rigorously preserving differential privacy. Our approach uniquely integrates highly interpretable decision trees into a differentially private federated learning framework and systematically quantifies the adverse impact of privacy-preserving noise on model interpretability. Experimental results demonstrate that FEXT-DP outperforms existing neural network–based federated learning methods in terms of training efficiency, prediction accuracy (measured by mean squared error), and interpretability.

Technology Category

Machine Learning: Distributed Machine Learning & Federated LearningNatural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsHumans and AI: Explainable AI (XAI) for Human Understanding

Application Category

User Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSystems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Data privacy and eXplainable Artificial Intelligence (XAI) are two important aspects for modern Machine Learning systems. To enhance data privacy, recent machine learning models have been designed as a Federated Learning (FL) system. On top of that, additional privacy layers can be added, via Differential Privacy (DP). On the other hand, to improve explainability, ML must consider more interpretable approaches with reduced number of features and less complex internal architecture. In this context, this paper aims to achieve a machine learning (ML) model that combines enhanced data privacy with explainability. So, we propose a FL solution, called Federated EXplainable Trees with Differential Privacy (FEXT-DP), that: (i) is based on Decision Trees, since they are lightweight and have superior explainability than neural networks-based FL systems; (ii) provides additional layer of data privacy protection applying Differential Privacy (DP) to the Tree-Based model. However, there is a side effect adding DP: it harms the explainability of the system. So, this paper also presents the impact of DP protection on the explainability of the ML model. The carried out performance assessment shows improvements of FEXT-DP in terms of a faster training, i.e., numbers of rounds, Mean Squared Error and explainability.

Problem

• Federated Learning
• Differential Privacy
• Explainability
• XAI
• Privacy-Explainability Trade-off

Innovation

• Federated Learning
• Differential Privacy
• Explainable AI
• Decision Trees
• Privacy-Explainability Trade-off