Federated learning enables multiple parties to contribute their locally trained models to an aggregation server, which securely combines individual models into a global one. However, it lacks a fair, verifiable, and proportionate reward (or penalty) mechanism for each contributor. Implementing a smart contract-based contribution analysis framework for federated learning on a privacy-enabled Ethereum L2 can address this challenge, and build the economics of federated learning public chain.