{
  "abstract": "Future mobile network management will depend heavily on the ability to learn and anticipate human behavior, including mobility and traffic demand, while preserving user privacy. Federated learning enables distributed spatio-temporal modeling and edge-based training without sharing raw data. However, existing approaches are mono-perspective, addressing mobility prediction, traffic forecasting, and network performance optimization as isolated tasks, with limited integration into network control and resource orchestration. This paper presents a vision architecture that integrates into the control loop of AI-native 6G networks, multi-perspective spatio-temporal representations of user mobility, traffic behavior, and network performance, derived through human-aware federated learning. The proposed architecture is an initial step toward scalable, privacy-preserving, and human-perceptive intelligence for 6G resource management.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Judy C. Guevara"
    },
    {
      "name": "Anne Josiane Kouam"
    },
    {
      "name": "Nadjib Achir"
    },
    {
      "name": "Aline Carneiro Viana"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c9yk4b4be0ecy.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "c9yk4b4be0ecy",
  "issns": [],
  "landing_url": "https://inria.hal.science/hal-05742398v2",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
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      "hal",
      "0.stat"
    ],
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  "openalex": null,
  "pdf_url": "https://inria.hal.science/hal-05742398/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c9yk4b4be0ecy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "hal-05742398",
      "source": "hal",
      "url": "https://inria.hal.science/hal-05742398v2"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    }
  ],
  "title": "Toward Human-Perceptive 6G: Privacy-Preserving Network Intelligence from Human Dynamics",
  "title_text": "Toward Human-Perceptive 6G: Privacy-Preserving Network Intelligence from Human Dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/c9yk4b4be0ecy",
  "venue": "HAL",
  "versions": []
}