codemeta.json

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "abstract": "Multi-agent active inference gives a natural account of belief sharing: agents hold local posteriors over a shared latent state, communicate those beliefs, and pool them into a colony-level consensus. The same mechanism is fragile when a member is miscalibrated, corrupted, or strategically wrong. Because the standard pool multiplies the reports together, a single confident-but-wrong broadcast that puts near-zero mass on the true state can pull the whole consensus off it, outweighing many honest members. The colony therefore needs a way to preserve the useful structure of belief sharing while limiting the influence of contaminated beliefs. This paper presents Active Fedference, a discrete-categorical framework that connects robust federated generalized variational inference with active inference belief sharing. The main bridge is structural: standard belief sharing appears as the non-robust corner of a broader generalized-Bayes family, while robust losses, conservative server fusion, and explicit aggregation diagnostics describe how the system moves away from that corner under declared contamination mechanisms. The result is not a replacement for belief sharing, but a containment result: ordinary belief sharing is recovered when robustness is turned off. Bounded-loss theory applies on the client axis, while the variational-server axis supplies an objective-backed redescending weight update. Following the manuscript's authoritative three-axis guarantee map, the paper keeps source-conditional client updates, a recovery-only heuristic server rule, and an objective-backed variational server rule as separate claim owners, with no robustness guarantee transferred between them. The study suite then exercises the framework as an end-to-end research system: recovery checks anchor the standard-Bayes limit, belief-sharing studies verify the communication baseline, contamination experiments test robust consensus, and extension studies probe moving agents, hierarchical latent structure, sensitivity to acuity and colony size, parameter recovery, and single-host socket-backed federation traces. All reported quantities are generated from deterministic analysis artifacts and injected into the manuscript by token, so the paper, figures, release package, and validation reports remain tied to the same execution record. The open-source repository is ActiveInferenceInstitute/Active_Fedference. This final-release manuscript uses assigned version DOI 10.5281/zenodo.22149133; the bound deposited PDF and repository release are controlled by separate publication gates.",
  "author": [
    {
      "@id": "https://orcid.org/0000-0001-6232-9096",
      "@type": "Person",
      "affiliation": {
        "@type": "Organization",
        "name": "Active Inference Institute"
      },
      "email": "daniel@activeinference.institute",
      "familyName": "Friedman",
      "givenName": "Daniel Ari"
    }
  ],
  "codeRepository": "https://github.com/ActiveInferenceInstitute/Active_Fedference",
  "dateCreated": "2026-06-24",
  "dateModified": "2026-09-19",
  "description": "Source code and reproducible artifacts for Active Fedference, a discrete-categorical research package connecting FedGVI to federated active-inference belief sharing.",
  "identifier": "https://doi.org/10.5281/zenodo.22149133",
  "keywords": [
    "active inference",
    "federated learning",
    "generalised variational inference",
    "belief sharing",
    "robustness",
    "FedGVI"
  ],
  "license": "https://spdx.org/licenses/MIT",
  "name": "Active Fedference",
  "programmingLanguage": [
    {
      "@type": "ComputerLanguage",
      "name": "Python",
      "version": "3.10+"
    }
  ],
  "version": "1.1.0"
}