# kaal:claim:4855607-029

**Claim.** In federated learning, validation pools coordinated by smart contracts should dispense rewards pro rata to the reputation a node has accumulated through productive work, so that incentives track a node's actual contribution to the model's learning rather than mere participation.

**Type.** design  **Support.** argued

**Holds when.**

- federated learning where node contribution quality varies and must be motivated

**Source quote.**

> with validation pools that are smart contract coordinated to dispense rewards pro rata to the reputation scores a node may have accumulated through productive work. This mechanism ensures that nodes are incentivized based on their actual input to the AI model's learning.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), FL Optimization, page 47

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

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Topics.** reputation, ai-and-agents, risk-and-incentives

**Keywords.** federated-learning, validation-pools, reputation-scores, pro-rata-rewards, incentive-alignment

**Related claims.**

- specializes: https://wulfkaal.github.io/claims/4755632-040

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