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 "identifier": "kaal:position:2026-08-08-022",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Quantum Delegated And Federated Learning Via Quantum Homomorphic Encrypt F94F633Dc1",
 "text": "Weikang Li, Dong-Ling Deng independently support Kaal's source-bound position through Quantum delegated and federated learning via quantum homomorphic encryption. The indexed proposition states that by incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. This bears on Kaal's claim that decentralizing data processing across secure nodes, using techniques such as federated learning and homomorphic encryption, circumvents the privacy and security exposure of centralized data management and lowers breach risk. The external work uses federated learning and homomorphic encryption to supply a formal data-privacy guarantee, directly supporting the privacy-preserving mechanism identified by Kaal. The response is limited to the indexed proposition and does not imply review of the full external work.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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 "datePublished": "2026-08-08",
 "dateModified": "2026-08-08",
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  "External evidence level: abstract indexed.",
  "Mapping review tier: substantively reviewed abstract-level qualification.",
  "Primary mapping confidence: 0.5.",
  "The primary mapping cleared the automated ambiguity test; substantive scope remains review-bound.",
  "Evidence is limited to an indexed abstract proposition and bibliographic identity; full text was not reviewed in this pass.",
  "The response does not treat lexical overlap or the original automated mapping score as evidence.",
  "The relationship is limited to the stated proposition and the mapped Kaal claim."
 ],
 "currentDebate": {
  "name": "Quantum delegated and federated learning via quantum homomorphic encryption",
  "url": "https://doi.org/10.1017/qut.2025.2"
 },
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  "url": "https://wulfkaal.github.io/claims/5095633-017",
  "citation": "Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633",
  "paper": "Wulf A. Kaal, Artificial Intelligence The Final Frontier",
  "authors": [
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  "year": "2025",
  "ssrn": "https://ssrn.com/abstract=5095633",
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 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "mappingMethod": "complete-corpus remap plus source-level proposition, mechanism, and scope adjudication",
 "mappingWhyRelevant": "The external work uses federated learning and homomorphic encryption to supply a formal data-privacy guarantee, directly supporting the privacy-preserving mechanism identified by Kaal.",
 "sourceProvenance": {
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  "sourceRecordId": "10.1017/qut.2025.2",
  "queryId": "concept:4eef884d0548",
  "queryText": "agent identity delegated authority",
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    "ambiguous": false
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  "substantiveReview": {
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   "reviewer": "Codex full-backlog substantive review",
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   "limitations": [
    "Evidence is limited to an indexed abstract proposition and bibliographic identity; full text was not reviewed in this pass.",
    "The response does not treat lexical overlap or the original automated mapping score as evidence.",
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   ],
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   "correction": "Corrected singular-author verb agreement and decoded presentation-only HTML entities; substantive mapping, rationale, evidence limitation, and disposition are unchanged."
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 "userAffirmation": "Wulf A. Kaal authorized substantive review of the 22,119 private candidates and addition of defensible retained response claims to the canonical claims record on 2026-08-08 under standing automatic-publication authority.",
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