kaal:claim:4855607-006

Federated learning does not eliminate privacy risk: because gradients and partial parameters are transmitted, the system remains vulnerable to attacks that leak data, and this vulnerability together with communication overhead is a significant hurdle to deployment.

Privacy concerns, reliance on batch-by-batch updates, vulnerability to data leaks caused by attacks due to the transfer of gradients and partial parameters, and communication overhead are significant hurdles that need to be overcome for the successful deployment of FL.

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Federated Machine Learning Models, p. 16
https://ssrn.com/abstract=4855607 · source PDF

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

• federated learning schemes that share gradients or partial parameters with a server

failuresupport: evidencedfailure: Gradient leakage in federated learningfamily: privacy-and-surveillance-riskconsensus-and-security

• extends kaal:claim:4796714-009 Federated learning does not eliminate privacy risk, because although the data stays decentralized the exchange...
• restated_by kaal:claim:4941807-012 Federated learning does not eliminate privacy risk, because although the data stays decentralized the protocol...
• extended_by kaal:claim:4941807-020 Privacy preserving frameworks such as federated learning do not fully solve centralization, because they typic...
• contested_by kaal:claim:5095633-017 Decentralizing data processing across secure nodes, using techniques such as federated learning and homomorphi...

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