# kaal:claim:4796714-009

**Claim.** Federated learning does not eliminate privacy risk, because although the data stays decentralized the exchange of model parameters can still expose sensitive information if those parameters are intercepted or improperly handled.

**Type.** failure  **Support.** evidenced

**Holds when.**

- applies to practical federated learning deployments
- risk arises at the parameter exchange step

**Source quote.**

> 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.

**From.** Wulf A. Kaal, *AI Governance* (2024), AI Models: Federated Model, page 12

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

**Verify.** sha256 of source PDF `59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance.pdf

**Failure mode.** parameter exchange leakage  (family: privacy-and-surveillance-risk)

**Topics.** consensus-and-security

**Keywords.** federated-learning, privacy, parameter-leakage, security

**Related claims.**

- restated_by: https://wulfkaal.github.io/claims/4941807-012
- extended_by: https://wulfkaal.github.io/claims/4855607-006

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.