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Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph\u2019s nodes. Subnetworks that are relevant to the classification (referred to as \u201cdisease subnetworks\u201d) are detected using explainable AI. Federated learning is enabled by dividing the knowledge graph into relevant subnetworks, constructing an ensemble classifier, and allowing domain experts to analyze and manipulate detected subnetworks using a developed user interface. Furthermore, the human-in-the-loop principle can be applied with the incorporation of experts, interacting through a sophisticated User Interface (UI) driven by Explainable Artificial Intelligence (xAI) methods, changing the datasets to create counterfactual explanations. The adapted datasets could influence the local model\u2019s characteristics and thereby create a federated version that distils their diverse knowledge in a centralized scenario. This work demonstrates the feasibility of the presented strategies, which were originally envisaged in 2021 and most of it has now been materialized into actionable items. In this paper, we report on some lessons learned during this project.<\/jats:p>","DOI":"10.1007\/978-3-031-40837-3_4","type":"book-chapter","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T23:02:25Z","timestamp":1692658945000},"page":"45-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Human-in-the-Loop Integration with\u00a0Domain-Knowledge Graphs for\u00a0Explainable Federated Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-5194","authenticated-orcid":false,"given":"Andreas","family":"Holzinger","sequence":"first","affiliation":[]},{"given":"Anna","family":"Saranti","sequence":"additional","affiliation":[]},{"given":"Anne-Christin","family":"Hauschild","sequence":"additional","affiliation":[]},{"given":"Jacqueline","family":"Beinecke","sequence":"additional","affiliation":[]},{"given":"Dominik","family":"Heider","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Roettger","sequence":"additional","affiliation":[]},{"given":"Heimo","family":"Mueller","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Baumbach","sequence":"additional","affiliation":[]},{"given":"Bastian","family":"Pfeifer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"issue":"9","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta, J.N., Falcone, G.J., Rajpurkar, P., Topol, E.J.: Multimodal biomedical AI. 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