Consistent Post-Hoc Explainability in Federated Learning through Federated Fuzzy Clustering

Ensuring trustworthiness of AI systems by enforcing, for instance, data privacy and model explainability, has become urgent in our society. Recently, the Federated Learning (FL) paradigm has been proposed to preserve data privacy during collaborative model learning. Unfortunately, FL poses critical challenges in the application of post-hoc explanation methods which are used to explain opaque models such as neural networks. In this paper we present an approach for enhancing the explainability of opaque models generated according to the FL paradigm. We focus on one of the most popular methods, namely SHapley Additive exPlanations method (SHAP). Given an input instance, SHAP can explain why an opaque model generated that specific output prediction from the input values. To provide the explanation SHAP needs access to a background dataset, typically consisting of representative training instances. In FL setting, however, the training data are scattered over multiple participants and cannot be shared due to privacy constraints. On the other side, the background dataset should be representative of the overall training set. To this aim, we propose to adopt a federated Fuzzy C-Means clustering for the generation of a common background dataset made up of cluster centers. The resulting background dataset is representative of the actual distribution of the data and can be made available to all participants without violating privacy, thus ensuring accuracy and consistency of the explanations. A thorough experimental analysis shows the validity of the proposed approach also in comparison with baseline and alternative approaches.