  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.
