# Decefl `kaal:entity:decefl` **Status.** derived This node is assembled mechanically from the 2 claims that carry the concept tag `decefl`. 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 2 claims across 2 works, 2024 to 2024. **2024** - [4796714-031](https://wulfkaal.github.io/claims/4796714-031) [empirical/evidenced] -- Decentralized Federated Learning reaches the global minimum with zero performance gap and matches the convergence rate of centralized methods 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 (2024). SSRN: https://ssrn.com/abstract=4796714 - [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/decefl.md | sha256sum **Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.