{
  "abstract": "Federated learning (FL) lets institutions train shared models without moving raw data, attractive for clinical electroencephalography (EEG), where cohorts are small and privacy-sensitive. Two obstacles persist: clinical sites record EEG with different electrode montages, so input feature spaces differ and standard aggregation fails; and because one recording yields thousands of epochs, splitting epochs rather than subjects leaks subject identity and inflates accuracy. We present MontageFL, a montage-agnostic pipeline that maps any electrode configuration to a fixed 88-dimensional feature vector through region-based aggregation of spectral and covariance features, letting a 19-channel and an 11-channel site train together. Benchmarking centralized, local, FedAvg, and FedProx training for Alzheimer’s disease versus healthy-control classification (Miltiadous et al. dataset; 65 subjects) under strictly subject-level, leakage-free evaluation, subject-level accuracy reached approximately 74% (19-channel) and 89% (11-channel), with no detected difference between federated and local models (FedProx versus local, $$-1.8$$ pp; 95% CI $$[-6.3, +2.7]$$ ), consistent with parity but unable to exclude a modest effect given the small cohort. Evaluated with epoch-level splitting, the same pipeline scored 15–30 points higher, illustrating leakage-driven inflation. Because montage heterogeneity was simulated by channel subsetting within one acquisition system, validation on independent recording hardware remains future work.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Medicine, Damascus University",
    "Faculty of Information Technology, Syrian Virtual University",
    "Faculty of Information Technology Engineering, Damascus University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bahaa Masry"
    },
    {
      "affs": [
        1
      ],
      "name": "Anas Shahin"
    },
    {
      "affs": [
        2
      ],
      "name": "George Karraz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/21yfbx6zp4wzp.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-65986-4",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-65986-4",
  "id": "21yfbx6zp4wzp",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-65986-4",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7202074445",
  "pdf_url": "https://www.nature.com/articles/s41598-026-65986-4_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/21yfbx6zp4wzp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:45Z",
      "native_id": "6cd8697ac65a480c8e2b4ef7107f7be5",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-65986-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Montage-agnostic federated learning for privacy-preserving Alzheimer’s disease classification from heterogeneous multi-site EEG",
  "title_text": "Montage-agnostic federated learning for privacy-preserving Alzheimer’s disease classification from heterogeneous multi-site EEG",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:45Z",
  "url": "https://research.draconitic.com/paper/21yfbx6zp4wzp",
  "venue": "Scientific Reports",
  "versions": []
}