Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy

TL;DR

This paper introduces FEXT-DP, a federated learning model based on decision trees that combines differential privacy with explainability, and analyzes how DP impacts interpretability using SHAP and MDI methods.

Contribution

It proposes a novel federated decision tree model with differential privacy and evaluates its effects on explainability metrics.

Findings

Differential Privacy reduces the explainability of the model.

Decision trees offer a lightweight and interpretable alternative to neural networks in FL.

The paper provides insights into the trade-off between privacy and interpretability in FL.

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, analyzing the obtained results for SHAP (SHapley Additive exPlanations) and Mean Decrease in Impurity (MDI).