{
  "abstract": "Clustered federated learning (CFL) is an effective paradigm for handling statistical heterogeneity by grouping clients with similar data characteristics and learning cluster-specific models. However, existing CFL methods often expose sensitive clustering signals or cluster-specific updates to the server, which may reveal latent client similarity relations and weaken privacy protection. To address this issue, we propose Privacy-Preserving Clustered Federated Learning (PPCFL), a split-stream framework that integrates adaptive Gaussian perturbation with threshold Paillier encrypted aggregation. In PPCFL, backbone updates are protected by adaptive Gaussian perturbation before plaintext aggregation, while clustering signatures and cluster-head updates are first perturbed by stream-specific adaptive Gaussian mechanisms and then uploaded under threshold Paillier encryption. The server performs ciphertext-domain aggregation for clustering prototypes and cluster-head updates, whereas plaintext prototypes and cluster-level decrypted aggregates are recovered by a qualified threshold-decryption client subset without giving the server decryption capability. In addition, PPCFL adopts round-wise budget growth, utility-aware refinement, and adaptive clipping-threshold updates to improve the privacy–utility trade-off under dynamic Non-IID settings. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 show that PPCFL achieves the highest final-round accuracy among the evaluated methods in the reported settings while providing enhanced protection for clustering-related information and cluster-specific updates. Under the representative Dirichlet setting $$\\alpha =0.5$$ , PPCFL improves the final accuracy over DP-FedAvg by 0.33, 1.73, and 2.62 percentage points on MNIST, Fashion-MNIST, and CIFAR-10, respectively, and over IFCA by 0.98, 8.28, and 10.24 percentage points.",
  "abstract_shown": true,
  "affiliations": [
    "School of Information Engineering, Jingdezhen University",
    "Cybersecurity and Information Technology Center, Jingdezhen University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jun Zhan"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenglong Jiang"
    },
    {
      "affs": [
        1
      ],
      "name": "Lang Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f9hemaswgs014.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63242-3",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63242-3",
  "id": "f9hemaswgs014",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63242-3",
  "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": "W7169877572",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63242-3_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/f9hemaswgs014.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:45Z",
      "native_id": "66654c69758246aba2af6ec1eea607a9",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-63242-3"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Privacy-preserving clustered federated learning via differential privacy and homomorphically encrypted prototypes",
  "title_text": "Privacy-preserving clustered federated learning via differential privacy and homomorphically encrypted prototypes",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:45Z",
  "url": "https://research.draconitic.com/paper/f9hemaswgs014",
  "venue": "Scientific Reports",
  "versions": []
}