# Decentralized ai

`kaal:entity:decentralized-ai`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `decentralized-ai`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 1 works, 2024 to 2024.

**2024**

- [4941807-012](https://wulfkaal.github.io/claims/4941807-012) [failure/argued] *(failure mode)* -- 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). SSRN: https://ssrn.com/abstract=4941807
- [4941807-013](https://wulfkaal.github.io/claims/4941807-013) [failure/argued] *(failure mode)* -- The rigid communication topology of federated learning, which requires constant coordination among numerous nodes, produces inefficiencies and does not adapt easily to dynamic network conditions or node failures.
  > Moreover, the rigid communication topology in FL, which typically requires constant coordination between numerous nodes, can lead to inefficiencies and does not easily adapt to dynamic network conditions or node failures.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807
- [4941807-020](https://wulfkaal.github.io/claims/4941807-020) [failure/argued] *(failure mode)* -- 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). SSRN: https://ssrn.com/abstract=4941807
- [4941807-026](https://wulfkaal.github.io/claims/4941807-026) [condition/evidenced] -- Decentralized Federated Learning lets every client reach the global minimum with zero performance gap and at the same convergence rate as centralized methods, but only when the loss function is smooth and strongly convex.
  > The DeceFL approach ensures that every client can reach the global minimum with zero performance gap and achieve the same convergence rate as centralized methods when the loss function is smooth and strongly convex.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/decentralized-ai.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.