SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare
Federated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information. The lack of transparency in federated models makes their deployment in healthcare settings more difficult, as know...
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MDPI AG
2025-05-01
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| author | Alba Amato Dario Branco |
| author_facet | Alba Amato Dario Branco |
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| description | Federated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information. The lack of transparency in federated models makes their deployment in healthcare settings more difficult, as knowledge of the decision process is of primary importance. This paper introduces SemFedXAI, a new framework that combines Semantic Web technologies and federated learning to achieve better explainability of artificial intelligence models in healthcare. SemFedXAI extends traditional FL architectures with three key components: (1) Ontology-Enhanced Federated Learning that enriches models with domain knowledge, (2) a Semantic Aggregation Mechanism that uses semantic technologies to improve the consistency and interpretability of federated models, and (3) a Knowledge Graph-Based Explanation component that provides contextualized explanations of model decisions. We evaluated SemFedXAI within the context of e-health, reporting noteworthy advancements in explanation quality and predictive performance compared to conventional federated learning methods. The findings refer to the prospects of combining semantic technologies and federated learning as an avenue for building more explainable and resilient AI systems in healthcare. |
| format | Article |
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| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-82747246ae1e4eb0894dd6f2ac2df7632025-08-20T02:20:58ZengMDPI AGInformation2078-24892025-05-0116643510.3390/info16060435SemFedXAI: A Semantic Framework for Explainable Federated Learning in HealthcareAlba Amato0Dario Branco1Department of Political Science, University of Campania “L. Vanvitelli”, 81100 Caserta, ItalyDepartment of Engineering, University of Campania “L. Vanvitelli”, 81031 Aversa, ItalyFederated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information. The lack of transparency in federated models makes their deployment in healthcare settings more difficult, as knowledge of the decision process is of primary importance. This paper introduces SemFedXAI, a new framework that combines Semantic Web technologies and federated learning to achieve better explainability of artificial intelligence models in healthcare. SemFedXAI extends traditional FL architectures with three key components: (1) Ontology-Enhanced Federated Learning that enriches models with domain knowledge, (2) a Semantic Aggregation Mechanism that uses semantic technologies to improve the consistency and interpretability of federated models, and (3) a Knowledge Graph-Based Explanation component that provides contextualized explanations of model decisions. We evaluated SemFedXAI within the context of e-health, reporting noteworthy advancements in explanation quality and predictive performance compared to conventional federated learning methods. The findings refer to the prospects of combining semantic technologies and federated learning as an avenue for building more explainable and resilient AI systems in healthcare.https://www.mdpi.com/2078-2489/16/6/435federated learningexplainable AIsemantic webknowledge managementhealthcareontologies |
| spellingShingle | Alba Amato Dario Branco SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare Information federated learning explainable AI semantic web knowledge management healthcare ontologies |
| title | SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare |
| title_full | SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare |
| title_fullStr | SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare |
| title_full_unstemmed | SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare |
| title_short | SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare |
| title_sort | semfedxai a semantic framework for explainable federated learning in healthcare |
| topic | federated learning explainable AI semantic web knowledge management healthcare ontologies |
| url | https://www.mdpi.com/2078-2489/16/6/435 |
| work_keys_str_mv | AT albaamato semfedxaiasemanticframeworkforexplainablefederatedlearninginhealthcare AT dariobranco semfedxaiasemanticframeworkforexplainablefederatedlearninginhealthcare |