Trustworthy AI in Heterogeneous Settings: Federated Learning of Explainable Classifiers

Trustworthy Artificial Intelligence (AI) has gained significant relevance worldwide. Federated Learning (FL) and eXplainable Artificial Intelligence (XAI) are two among the most relevant paradigms for accomplishing the requirements of trustworthy AI-based applications. On the one hand, FL guarantees data privacy throughout a collaborative learning of an AI model from decentralized data. On the other hand, XAI models ensure transparency, accountability, and trust in AI-based systems by providing understandable explanations for their predictions and decisions. To the best of our knowledge, only few works have explored the combination of FL with inherently explainable models, especially for classification task. In this work, we investigate FL of explainable classifiers, namely Fuzzy Rule-based Classifiers. In the proposed FL scheme, each participant creates its own set of classification rules from its own local training data, resorting to a simple procedure that generates a rule for each training instance. Local rules are sent to a central server which is in charge of aggregating them by removing duplicates and solving conflicts. The aggregated set of rules is then forwarded to the single participants for inference purposes. In our experimental analysis we consider two real-world case studies focusing on heterogeneous settings, namely non-IID (Independent and Identically Distributed) scenarios. Our FL scheme offers significant advantages in terms of classification performance to the participants in the federation, preserving data privacy.