kaal:claim:4941807-012

Federated learning does not eliminate privacy risk, because although the data stays decentralized the protocol still exchanges model parameters, and those parameters can expose sensitive information if intercepted or improperly handled.

These challenges arise because, while FL keeps data decentralized, it still involves the exchange of model parameters, which could potentially expose sensitive information if intercepted or improperly handled.

Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024), Federated Model, p. 12
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 deployments that exchange model parameters
• where parameters can be intercepted or mishandled

failuresupport: arguedfailure: parameter exchange leakagefamily: privacy-and-surveillance-riskconsensus-and-securitydecentralizationai-and-agents

• restates kaal:claim:4796714-009 Federated learning does not eliminate privacy risk, because although the data stays decentralized the exchange...
• contested_by kaal:claim:5095633-017 Decentralizing data processing across secure nodes, using techniques such as federated learning and homomorphi...
• restates kaal:claim:4855607-006 Federated learning does not eliminate privacy risk: because gradients and partial parameters are transmitted, ...

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