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 heterogeneity. 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.

翻译：随着现代世界数字化与智能化的不断推进，基于大数据驱动的智能产业与智慧服务需求急剧增长。本综述全面审视了区块链联邦学习（BlockFL）技术，该技术融合了区块链与联邦学习的优势，为上述需求提供了安全高效的解决方案。我们针对四种物联网应用场景——个人物联网（PIoT）、工业物联网（IIoT）、车联网（IoV）及健康物联网（IoHT）中的现有BlockFL模型进行了比较研究，重点分析了安全性、隐私性、信任可靠性、效率及数据异构性等维度。分析表明，去中心化与透明性特征使BlockFL成为分布式模型训练的安全有效方案，但通信开销与兼容性问题仍需深入探究。研究同时揭示了各领域特有的挑战，例如IoV需适应动态环境、IoHT对身份权限管理的高要求，以及隐私约束、资源限制、数据异构性等共性难题。此外，我们系统梳理了可赋能BlockFL的现有技术，旨在帮助研究人员与从业者在不同物联网应用场景中，对BlockFL的选型与开发做出明智决策。