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.

翻译：数据隐私与可解释人工智能（XAI）是现代机器学习系统的两个重要方面。为增强数据隐私，近期机器学习模型被设计为联邦学习（FL）系统。在此基础上，可通过差分隐私（DP）添加额外的隐私保护层。另一方面，为提升可解释性，机器学习必须采用特征数量更少、内部架构更简洁的可解释方法。在此背景下，本文旨在实现一种兼具增强数据隐私与可解释性的机器学习（ML）模型。为此，我们提出一种名为“基于差分隐私的联邦可解释树”（FEXT-DP）的联邦学习解决方案，其特点在于：（i）以决策树为基础——因其轻量级特性且相比基于神经网络的联邦学习系统具有更优的可解释性；（ii）通过对树模型应用差分隐私（DP）提供额外的数据隐私保护层。然而，引入DP会产生副作用：损害系统的可解释性。因此，本文亦揭示了DP保护对机器学习模型可解释性的影响。性能评估结果表明，FEXT-DP在训练速度（即通信轮次）、均方误差及可解释性方面均有所提升。