Privacy enabled, Smart Contract driven Fair and transparent reward mechanism in Federated AI

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.