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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.<\/jats:p>","DOI":"10.1145\/3659099","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T12:24:21Z","timestamp":1713183861000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["Blockchained Federated Learning for Internet of Things: A Comprehensive Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-6264","authenticated-orcid":false,"given":"Yanna","family":"Jiang","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4167-2797","authenticated-orcid":false,"given":"Baihe","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9439-6437","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6111-1607","authenticated-orcid":false,"given":"Guangsheng","family":"Yu","sequence":"additional","affiliation":[{"name":"CSIRO, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-873X","authenticated-orcid":false,"given":"Ping","family":"Yu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0625-770X","authenticated-orcid":false,"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Xidian University, Xian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0780-4637","authenticated-orcid":false,"given":"Wei","family":"Ni","sequence":"additional","affiliation":[{"name":"CSIRO, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7001-6305","authenticated-orcid":false,"given":"Ren Ping","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT51234.2021.9370566"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2444095"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJPCC-03-2022-0119"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23177476"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102355"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2886932"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3050241"},{"issue":"5","key":"e_1_3_1_9_2","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono Yoshinori","year":"2017","unstructured":"Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai. 2017. 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