{
  "abstract": "Multimodal fusion is studied under hybrid federation, where data are partitioned across samples and feature blocks. The proposed two-dimensional federated smooth multiple-kernel large-margin learning method (2D-FedSMKL) integrates availability-corrected modality scores, group-kernel regularization, and smooth worst-site risk aggregation. For fixed maps and propensities, the objective is convex and smooth. Curvature bounds and exact synchronous block-gradient equivalence are established for binary and multiclass scores, giving a conditional accelerated rate. An oracle-propensity binary risk bound and a propensity-perturbation result are also derived. Simulations and two public activity-recognition benchmarks assess site heterogeneity and modality absence. Worst-site aggregation improves worst-site performance under heterogeneity, including reversed and size-balanced designs. On the benchmarks, validation selects the correction; the selected model is not significantly worse than the strongest reference at the worst site, and group-regularization benefits vary by dataset.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03032",
  "author_total": 3,
  "authors": [
    {
      "name": "Xiaofei Wu"
    },
    {
      "name": "Rongmei Liang"
    },
    {
      "name": "Jian Qing Shi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0277xqcyk3qtj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03032",
  "fulltext_url": null,
  "id": "0277xqcyk3qtj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03032",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03032",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0277xqcyk3qtj.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.03032",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03032"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity",
  "title_text": "Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/0277xqcyk3qtj",
  "venue": "arXiv",
  "versions": []
}