On-device explainable artificial intelligence for the semantic web of everything

• Five-star framework to assess On-Device AI in Internet of Everything scenarios • Mafalda 2.0 federated incremental learning model with semantic explainability • Evaluation of Mafalda 2.0 against state-of-the-art On-Device AI approaches • Concrete examples of global and local explainability and federated learning As the Internet of Things (IoT) evolves into an Internet of Everything (IoE), adapting Artificial Intelligence (AI) and Machine Learning (ML) approaches to pervasive computing devices is not enough. Collaborative intelligence is required, calling for on-device AI frameworks combining adequate accuracy and computational efficiency levels with incremental learning on continuous data streams, federated learning in distributed architectures and symbolic explainability formalisms to foster trustworthiness with interpretable trained models and comprehensible prediction outcomes. To fill this gap, the paper introduces a five-star rating for on-device AI based on the Semantic Web of Everything (SWoE) paradigm, and presents the five-star Mafalda 2.0 framework. It combines statistical data processing with Knowledge Graph technologies for information representation and automated reasoning to support: semi-automatic or fully data-driven ontology definition; on-device training to generate highly interpretable semantics-based models; prediction framed as a semantic matchmaking problem, exploiting non-standard reasoning services endowed with logic-based justifications to provide comprehensible results as well as counterfactual and contrastive explanations. An experimental campaign on four publicly available datasets has been carried out to validate the efficiency and accuracy of the proposal, along with federated learning and explainability examples.