Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.

联邦学习（FL）近年来经历了繁荣，这是机器学习和人工智能的繁荣以及新兴的隐私问题共同推动的。在 FL 范例中，中央服务器和本地终端设备通过交换模型更新而不是原始数据来维护相同的模型，这样不会直接泄露存储在终端设备上的数据的隐私。通过这种方式，可以减轻因敏感数据收集的增加而导致的隐私侵犯。然而，具有中央服务器的 FL 的性能正在达到瓶颈，同时新的威胁也在不断涌现。原因多种多样，其中最重要的是中心化处理、数据造假和缺乏激励。为了加速FL的扩散，基于区块链的 FL 引起了学术界和工业界的广泛关注。大量新颖的解决方案被设计出来，以满足不同场景的新兴需求。支持区块链的 FL 提供了从各个角度提高 FL 性能的理论和技术。在本次调查中，我们将全面总结和评估支持区块链的 FL 的现有变体，确定新出现的挑战，并在这个尚未探索的领域提出潜在有前途的研究方向。