An illustration of the BCFL system. Download Scientific Diagram
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Download scientific diagram | Topology of fully coupled BCFL. from publication: Blockchain-based Federated Learning: A Comprehensive Survey | With the technological advances in machine learning.. The structure of this paper is outlined as follows. Section 2 introduces related survey studies, while Section 3 delves into the methodology employed for conducting this survey. In section 4, we overview both FL and Blockchain main concepts.In Section 5, we analyze the state-of-the-art regarding the integration of FL and Blockchain technology, describing the main architectures.
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With the wide adoption of cryptocurrency, blockchain technologies have become the foundation of such digital currencies. However, this adoption has been accompanied by a surge in cryptocurrency fraud, causing significant losses to financial organizations and individuals. One way to mitigate these losses is to use Federated Learning (FL) techniques to detect fraudulent cryptocurrency.. Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of.
![Cellresolved canister corrosion depth [m] at 100 000 years predicted](https://www.researchgate.net/profile/Marek-Pekala/publication/335362175/figure/fig41/AS:1055508978991113@1628664359500/Cell-resolved-canister-corrosion-depth-m-at-100-000-years-predicted-by-the-model-for.png "Cellresolved canister corrosion depth [m] at 100 000 years predicted")
![[2306.10841] BlockchainEnabled Federated Learning A Reference](https://ar5iv.labs.arxiv.org/html/2306.10841/assets/full-architecture.png "[2306.10841] BlockchainEnabled Federated Learning A Reference")
