{
  "abstract": "IntroductionRare and heterogeneous disease research increasingly relies on privacy-enhancing technologies such as federated learning (FL) to enable cross-institutional collaboration across fragmented datasets. However, data quality assurance in FL can be limited, as individual-level data may not be directly accessible. While schema-dependent inspection offers a partial solution, it requires standardised schemas or resource-intensive frameworks, hindering scalability and collaboration. To address these challenges, we implemented two complementary dashboards and evaluated their interplay.\n\nMethodologyWe adapted a recognised data quality control framework for re-use of electronic health record (EHR) data using breast cancer records from Centre Leon Berard into two dashboards: (1) an in-house dashboard for intra-clinic quality assessment, and (2) a federated dashboard for inter-clinic quality assessment. Artificial inconsistencies were introduced into distributed datasets mirroring the inhouse source to evaluate detection capabilities.\n\nResultsThe in-house dashboard provided granularity and reliability, pinpointing individual-level inconsistencies, while the federated dashboard enabled cross-institutional pattern detection - trade-offs inherent to their designs. The federated system revealed ecosystem-wide trends inaccessible to single-institution tools, whereas the in-house dashboard provided local validation and thoroughness.\n\nDiscussionOur findings confirm a complementary relationship: FL dashboards provide scalable, collaborative oversight but may require additional quality checks, while in-house tools ensure thoroughness at the cost of scalability. A combined model seemingly offers the optimal balance, accommodating both institution-specific needs and collaborative research requirements in evolving, multi-institutional ecosystems.",
  "abstract_shown": true,
  "affiliations": [
    "Maastricht University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Hogenboom"
    },
    {
      "name": "N. Perez"
    },
    {
      "name": "Q. Filori"
    },
    {
      "name": "A. Sans"
    },
    {
      "name": "A. Lobo Gomes"
    },
    {
      "name": "A. Dekker"
    },
    {
      "name": "W. van der Graaf"
    },
    {
      "name": "O. Husson"
    },
    {
      "name": "H. Crochet"
    },
    {
      "name": "L. Wee"
    },
    {
      "name": "V. Gouthamchand"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fd04a5m0jxgms.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.26361592",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361592v1.full",
  "id": "fd04a5m0jxgms",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361592v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1803|Data Quality and Management"
    ]
  ],
  "openalex": "W7213458335",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361592v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fd04a5m0jxgms.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.64898/2026.09.08.26361592",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26361592v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Data Auditing and Quality Assurance in a Federated Learning Consortium; Getting the Best of Both Worlds from Cross-Institutional and In-House Data Quality Inspection",
  "title_text": "Data Auditing and Quality Assurance in a Federated Learning Consortium; Getting the Best of Both Worlds from Cross-Institutional and In-House Data Quality Inspection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/fd04a5m0jxgms",
  "venue": "medRxiv",
  "versions": []
}