kaal:claim:4796714-028

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.

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.

Wulf A. Kaal, AI Governance (2024), Adapting Decentralization of AI Governance to AI Models, p. 33
https://ssrn.com/abstract=4796714 · source PDF

Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

• applies to standard federated learning architectures with a central aggregator

failuresupport: evidencedfailure: residual central aggregatorfamily: recentralization-driftdecentralization

• restated_by kaal:claim:4941807-020 Privacy preserving frameworks such as federated learning do not fully solve centralization, because they typic...

The quote above is an exact substring of the source PDF, whose sha256 is 59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93. Extraction method: pdf-text-layer.
Attestation record: colloquium/attestations/3ec08b9c4a24e764...json
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/4796714-028.md | sha256sum