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 "@id": "https://wulfkaal.github.io/entities/communication-overhead",
 "identifier": "kaal:entity:communication-overhead",
 "name": "Communication overhead",
 "termCode": "communication-overhead",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4796714-028",
   "identifier": "kaal:claim:4796714-028",
   "text": "Privacy preserving frameworks such as federated learning do not fully remove centralization, because they still typically depend on a central client to collect and distribute model information, which reintroduces high communication loads and centralized vulnerabilities.",
   "abstract": "In response, privacy-preserving frameworks like federated learning have been developed, yet these often still depend on a central client to collect and distribute model information, resulting in high communication loads and centralized vulnerabilities.",
   "citation": "Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714",
   "datePublished": "2024",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "applies to standard federated learning architectures with a central aggregator"
   ],
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   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4941807-020",
   "identifier": "kaal:claim:4941807-020",
   "text": "Privacy preserving frameworks such as federated learning do not fully solve centralization, because they typically still depend on a central client to collect and distribute model information, which produces high communication loads and reintroduces centralized vulnerabilities.",
   "abstract": "In response, privacy-preserving frameworks like federated learning have been developed, yet these often still depend on a central client to collect and distribute model information, resulting in high communication loads and centralized vulnerabilities.",
   "citation": "Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807",
   "datePublished": "2024",
   "claim_type": "failure",
   "confidence": "argued",
   "is_failure_mode": true,
   "scope_conditions": [
    "in federated learning designs that retain a central aggregating client"
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 ],
 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'communication-overhead'. Derived node: a roster, not an adjudicated definition."
}