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Federated learning (FL), as an excellent distributed machine learning paradigm, has gradually entered the public eye. FLs purpose is to solve the difficulty of centralized management of distributed data in real life and the prominent issues of data privacy, as well as data security. In FL, the central server distributes training tasks to clients to facilitate the training process. The client trains data locally and only uploads the updated model of local training to the central server, which is able to protect local data security effectively. In spite of that, FL still have disadvantages over high single-point failure, as well as lacking incentive mechanism. Thus, due to excellent decentralized nature of blockchain, a positive and feasible solution appears because of the technology combined with blockchain. In this article, we will introduce FL systems based on blockchain (BFL) and the current status of this field in detail. Specifically, we will first discuss the system coupled structure of BFL. Then, we will provide more details on challenges in BFL and corresponding solutions and explain main applications of BFL in various industries. Finally, we will discuss the difficult problems faced by BFL and give promising research directions in the future.
Wang et al. (2024) studied this question.