Federated learning, as a new paradigm of distributed machine learning, has become a key technological pathway that balances data usability and privacy security by utilizing the characteristic of data availability without exposure. However, ensuring the credibility of data sources, training processes, and training outcomes has become an important dimension for assessing the feasibility of engineering applications of federated learning. Blockchain utilizes a specific chain structure and cryptographic algorithms to create a decentralized trust mechanism. With its characteristics of transparency, smart contract automation, etc., naturally aligns with federated learning and provides a new solution for achieving trusted federated learning. Blockchain-based trusted federated learning offers significant advantages in data traceability, process compliance, and incentive allocation. It is also important to address the potential risks associated with blockchain technology itself as a new source of risk in the data flow process. Sufficient maturity and security in consensus and data communication are necessary for it to become the infrastructure of the data asset market.