Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy

ArXi:2602.10100v2 Announce Type: replace 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 interpretable approaches with reduced number of features and less complex internal architecture.