A survey on blockchain-enabled federated learning and its prospects with digital twin

dc.contributorAalto Universityen
dc.contributor.authorLiu, Kangdeen_US
dc.contributor.authorYan, Zhengen_US
dc.contributor.authorLiang, Xueqinen_US
dc.contributor.authorKantola, Raimoen_US
dc.contributor.authorHu, Chuangyueen_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.departmentDepartment of Information and Communications Engineeringen
dc.contributor.groupauthorNetwork Security and Trusten
dc.contributor.organizationXidian Universityen_US
dc.contributor.organizationYangtze Delta Region Blockchain Technology Research Instituteen_US
dc.descriptionPublisher Copyright: © 2022 Chongqing University of Posts and Telecommunications
dc.description.abstractDigital Twin (DT) supports real time analysis and provides a reliable simulation platform in the Internet of Things (IoT). The creation and application of DT hinges on amounts of data, which poses pressure on the application of Artificial Intelligence (AI) for DT descriptions and intelligent decision-making. Federated Learning (FL) is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training. Therefore, DT can benefit by combining with FL, successfully solving the “data island” problem in traditional AI. However, FL still faces serious challenges, such as enduring single-point failures, suffering from poison attacks, lacking effective incentive mechanisms. Before the successful deployment of DT, we should tackle the issues caused by FL. Researchers from industry and academia have recognized the potential of introducing Blockchain Technology (BT) into FL to overcome the challenges faced by FL, where BT acting as a distributed and immutable ledger, can store data in a secure, traceable, and trusted manner. However, to the best of our knowledge, a comprehensive literature review on this topic is still missing. In this paper, we review existing works about blockchain-enabled FL and visualize their prospects with DT. To this end, we first propose evaluation requirements with respect to security, fault-tolerance, fairness, efficiency, cost-saving, profitability, and support for heterogeneity. Then, we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements. Finally, we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL, based on which we further propose some directions for future research.en
dc.description.versionPeer revieweden
dc.identifier.citationLiu, K, Yan, Z, Liang, X, Kantola, R & Hu, C 2024, ' A survey on blockchain-enabled federated learning and its prospects with digital twin ', Digital Communications and Networks, vol. 10, no. 2, pp. 248-264 . https://doi.org/10.1016/j.dcan.2022.08.001en
dc.identifier.otherPURE UUID: e9912bf5-a05d-4bb2-8ab3-a73a2d9f81d1en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e9912bf5-a05d-4bb2-8ab3-a73a2d9f81d1en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85189683767&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/146668502/1-s2.0-S2352864822001626-main.pdfen_US
dc.publisherChongqing University of Posts and Telecommunications
dc.relation.ispartofseriesDigital Communications and Networks
dc.relation.ispartofseriesVolume 10, issue 2, pp. 248-264
dc.subject.keywordArtificial intelligenceen_US
dc.subject.keywordDigital twinen_US
dc.subject.keywordFederated learningen_US
dc.titleA survey on blockchain-enabled federated learning and its prospects with digital twinen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi