# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Scenarios of Blockchain-Based Federated Learning  
The BFL architecture has been applied to many services and domains, which are summarized and analyzed in this article. Table 4 lists the application scenarios and functions of BFL. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Application Direction** & **Goal** & **Method** & **Reference** \\ \hline BicT & \(\bullet\) & Industrial data transmission & \(\bullet\) & Separate the local model updating process from the mobile device-independent process. & [22] \\  & & & \(\bullet\) & Add an edge server to make most of the computation on the server, which improves the learning efficiency. \\  & & & \(\bullet\) & Use a distributed architecture of the blockchain to protect data security and privacy. \\  & & & \(\bullet\) & Design the application model of blockchain-based federated learning in the industrial Internet of Things (flict) and develop our data protection aggregation scheme based on the model. \\  & & & \(\bullet\) & Grey distributed K-means clustering, random forest, and AdaBoost to realize multiple data protection in data sharing and model sharing. \\  & & & \(\bullet\) & Integrate the methods with blockchain and federated learning and provide the complex security analysis. \\  & & & \(\bullet\) & Propose an efficient demanewion hash scheme for secure federated learning in Industrial Internet of Things. \\  & & & \(\bullet\) & Limit the use of trapdeno. The data owner can choose the validity period of the trap in a flexible way to improve the security of the model. \\ \hline Facility Internet of Things & \(\bullet\) & Model of migration & \(\bullet\) & Develop an incentive mechanism considering the economic benefits of fast devices. \\  & & & \(\bullet\) & Design a clustering-based algorithm for identifying malicious devices and preventing them from defreading incentives. \\  & & & \(\bullet\) & Use blockchain to ensure trustworthiness in model migration and incentive processes. \\  & & & \(\bullet\) & Present a platform architecture of blockchain-based federated learning systems for failure detection in IoT, which enables evitable integrity of client data. \\  & & & \(\bullet\) & Propose a novel control distance weighted federated averaging (CDW-FedAvg) algorithm to address the data heterogeneity issue in lidar failure detection. \\ \hline Power Internet of Things & \(\bullet\) & Space-Air-Cloud-Integrated & \(\bullet\) & The task offloading is decoupled from computing resource allocation through Lyapunov optimization. \\  & & & & \(\bullet\) \\  & & & \(\bullet\) & The task offloading problem is solved by the proposed electromagnetic interference sensing task offloading algorithm (FDAC-EMI) based on federated deep participant critics. \\  & & & \(\bullet\) & Solve the resource allocation problem by smoothing approximation and Lagrangian optimization. \\ \hline Internet of Vehicles & \(\bullet\) & Vehicle information dissemination & \(\bullet\) & Vehicles compete to become relay nodes (timers) by processing the proposed federated learning proof (PoFL) consensus embedded in the blockchain smart contract. \\  & & & \(\bullet\) & Develop a hybrid blockchain architecture to enhance the security and reliability of model parameters. In addition, progress an asynchronous federated learning scheme that uses Deep Reinforcement Learning (DRL) for node selection to improve efficiency. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Application directions of blockchain-based federated learning.