{
  "abstract": "Linear B-cell epitopes are antigenic regions recognised by antibodies that play crucial roles in vaccine development, antibody production, and other therapeutic applications.Their prediction can support peptide-based vaccine candidate screening and diagnostic assay development, but the experimental data required for model development may be distributed across institutions that cannot freely pool their records. We present FedEpitope, a federated framework that adapts the ESM-2 protein language model for peptide-level linear B-cell epitope prediction using Low-Rank Adaptation (LoRA), keeping client-partitioned data local and restricting the communicated trainable model state to approximately 1.11 MB. Simulated benchmarks of FedAvg and FedProx algorithms, evaluated using homology-aware data splits of five biologically heterogeneous, organism-partitioned clients, achieve promising global test performance without centralising training data, with FedAvg outperforming all single-client models in mean AP, F1, and MCC. Additional experiments characterise post-federation personalisation, record-level differential privacy, sensitivity to controlled client heterogeneity, LoRA rank, and generalisation to the independent BCPreds and NetBCE benchmarks, demonstrating the feasibility of parameter-efficient federated protein language model adaptation for data-localised B-cell epitope prediction and the associated trade-offs among predictive performance, personalisation, privacy, communication efficiency, and distribution shift.",
  "abstract_shown": true,
  "affiliations": [
    "Indraprastha Institute of Information Technology Delhi"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. Joshi"
    },
    {
      "name": "L. Priyadarsinee"
    },
    {
      "name": "B. Chatterjee"
    },
    {
      "name": "A. M. Natarajan"
    },
    {
      "name": "G. N. Sastry"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7jp2t1sbfkqca.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.31.763304",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763304v1.full",
  "id": "7jp2t1sbfkqca",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763304v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763304v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7jp2t1sbfkqca.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:14Z",
      "native_id": "10.64898/2026.10.31.763304",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763304v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction",
  "title_text": "FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:43Z",
  "url": "https://research.draconitic.com/paper/7jp2t1sbfkqca",
  "venue": "bioRxiv",
  "versions": []
}