Differentially Private FastSHAP for Federated Learning Model Explainability

Explaining the reasoning behind black-box model predictions while preserving user privacy is a significant challenge. This becomes even more complex in Federated Learning, where legal constraints restrict the data that clients can share with external entities. In this paper, we introduce FastSHAP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup>, a method that adapts FastSHAP to explain Federated Learning trained models. Unlike existing approaches, FastSHAP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> mitigates client privacy risks by incorporating Differential Privacy into the explanation process and preventing the exchange of sensitive information between clients and external entities. We evaluate the effectiveness of FastSHAP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> testing it on three different datasets, and comparing the explanations with those produced by a centralized explainer with access to clients’ training data. Lastly, we study the impact of varying levels of Differential Privacy to analyse the trade-offs between privacy and the quality of the explanations.