As an emerging decentralized distributed machine learning paradigm, blockchain based federated learning not only overcomes the shortcomings of traditional federated learning such as data silos, privacy breaches, and security threats, but also faces new challenges in terms of cost, efficiency, and effectiveness brought by blockchain technology. Therefore, this article first elaborated on federated learning and blockchain by combining basic principles, technical classifications, complementary advantages, and unresolved problems. On this basis, a systematic summary and analysis of current research on blockchain based federated learning was conducted around the architecture, performance, privacy, security, incentive mechanisms, consensus mechanisms, and applications involved in the combination of federated learning and blockchain. Finally, starting from the three dimensions of blockchain based federated learning itself, balance, and application, explored its future research trends and the main problems that urgently need to be solved.
