kaal:claim:4941807-020

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.

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 Via Web3 Reputation System (2024), Adapting Decentralization of AI Governance to AI Models, p. 24
https://ssrn.com/abstract=4941807 · source PDF

Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

• in federated learning designs that retain a central aggregating client

failuresupport: arguedfailure: residual central client dependencyfamily: recentralization-driftdecentralizationconsensus-and-securityai-and-agents

• restates kaal:claim:4796714-028 Privacy preserving frameworks such as federated learning do not fully remove centralization, because they stil...
• extends kaal:claim:4855607-006 Federated learning does not eliminate privacy risk: because gradients and partial parameters are transmitted, ...
• contested_by kaal:claim:5095633-017 Decentralizing data processing across secure nodes, using techniques such as federated learning and homomorphi...

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