{
  "abstract": "Aggregate accuracy and conventional attack success rates cannot distinguish pre-existing diagnostic errors from melanoma false negatives induced by a trigger. We evaluate melanoma-to-nevus risk downgrading in source-heterogeneous federated learning using paired clean and triggered predictions. Four source-defined clients represent clinical photography, smartphone imaging, and dermoscopy. The primary endpoint, trigger-induced paired false negatives (trigFN), counts clean-correct melanoma cases that change to a nevus prediction after triggering. In centralized screening, sticker and blend triggers produce 35 and 31 trigFN among 77 melanoma cases, whereas a square trigger produces none. Across four seeds, Full-Phase poisoning produces 16.25±10.14 trigFN and a 21.1±13.2-percentage-point melanoma-sensitivity loss; Late-Phase poisoning produces 4.75±2.99 trigFN and a 6.2±3.9-point loss. Under Full-Phase poisoning, NormClip with c=1 reduces trigFN to 2.75±3.20 and the sensitivity loss to 3.6±4.2 points, while mean balanced accuracy (BACC) changes from 0.732±0.026 to 0.708±0.034. External evaluation shows smaller within-model trigger-associated losses with clipping, although absolute melanoma sensitivity on untriggered images is lower. Paired clinical endpoints reveal harm that aggregate utility can obscure. Within the evaluated four-client, single-attacker, non-adaptive setting, norm-constrained aggregation reduces but does not eliminate harm.",
  "abstract_shown": true,
  "affiliations": [
    "College of Information Science and Engineering, Hohai University, Changzhou 213200, China",
    "Second Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing 210023, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ziyu Wang",
      "orcid": "0000-0002-7793-6275"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinyu Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Hongmei Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Chang Xu",
      "orcid": "0000-0002-5968-9290"
    },
    {
      "affs": [
        0
      ],
      "name": "Xuewu Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2nd4yavy56aq2.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213870",
  "fulltext_url": null,
  "id": "2nd4yavy56aq2",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3870",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Adversarial Robustness in Machine Learning"
    ]
  ],
  "openalex": "W7223676142",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/2nd4yavy56aq2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14213870",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3870"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Mathematical Analysis of Backdoor-Induced Melanoma Risk in Modality-Heterogeneous Federated Learning",
  "title_text": "Mathematical Analysis of Backdoor-Induced Melanoma Risk in Modality-Heterogeneous Federated Learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/2nd4yavy56aq2",
  "venue": "Mathematics",
  "versions": []
}