# kaal:claim:4941807-020

**Claim.** 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.

**Type.** failure  **Support.** argued

**Holds when.**

- in federated learning designs that retain a central aggregating client

**Source quote.**

> 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.

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), Adapting Decentralization of AI Governance to AI Models, page 24

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

**Verify.** sha256 of source PDF `ab66c1e99a88da1fa36b0c6b536df5184231fe6aa427f3dd53287a4e0ac79853` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance%20Via%20Web3%20Reputation%20System.pdf

**Failure mode.** residual central client dependency  (family: recentralization-drift)

**Topics.** decentralization, consensus-and-security, ai-and-agents

**Keywords.** federated-learning, centralization, communication-overhead, privacy, decentralized-ai

**Related claims.**

- restates: https://wulfkaal.github.io/claims/4796714-028
- extends: https://wulfkaal.github.io/claims/4855607-006
- contested_by: https://wulfkaal.github.io/claims/5095633-017

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