Federated learning () is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and as the Blockchain-based federated learning () framework. This paper introduces an in-depth survey of and discusses the insights of such a new paradigm. In particular, we first briefly introduce the technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of . Furthermore, we present the attempts ins improving performance with Blockchain and several combined applications of incentive mechanisms in . Finally, we summarize the industrial application scenarios of .