The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

随着当今世界数字化、智能化程度不断加深，基于大数据的智能产业和智能服务需求快速增长。这项调查全面回顾了区块链联邦学习 (BlockFL)，它结合了区块链和联邦学习的优势，为需求提供安全高效的解决方案。我们比较了个人物联网 (PIoT)、工业物联网 (IIoT)、车联网 (IoV) 和健康物联网 (IoHT) 四种物联网 (IoT) 应用场景中现有的 BlockFL 模型，重点关注安全和隐私、信任和可靠性、效率和数据多样性。我们的分析表明，去中心化和透明的特性使BlockFL成为分布式模型训练的安全有效的解决方案，但开销和兼容性仍需要进一步研究。它还揭示了每个领域的独特挑战，例如，除了确定的一些常见挑战（例如隐私、资源限制、和数据异构性。此外，我们还研究了可以使 BlockFL 受益的现有技术，从而帮助研究人员和从业者就针对各种物联网应用场景选择和开发 BlockFL 做出明智的决策。