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 "name": "Streaming Legal Logic Of Ai Data Governance Based On Federated Learning Institutio B32E395113",
 "text": "Legal Logic of AI Data Governance Based on Federated Learning: Institutional Evolution from Privacy Protection to Rights Distribution presents the following source proposition: The study finds that federated learning is not merely a technical tool but also an opportunity to drive legal institutional design innovation. This proposition is pertinent to Kaal's source-bound claim that In federated learning the communication cost of many edge devices sending model parameters to a central server frequently exceeds the computation cost, and heterogeneity in the participating devices, including varying computational capabilities and resource constraints, compounds the problem. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4436.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "url": "https://doi.org/10.64229/efs37007"
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  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
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  "ssrn": "https://ssrn.com/abstract=4855607",
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