# 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
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Application Direction** & **Goal** & **Method** & **Reference** \\ \hline Beyond-SG applications & \(\bullet\) & Edge intelligence of UAV & \(\bullet\) & Focus on investigating the synergy between FL and blockchain to realize autonomous UAV edge intelligence. & [123] \\  & & & The method includes multi-party federated learning techniques and an efficient cooperative learning framework. & \\  & & & Use smart contracts for resource allocation to make decisions with specific objectives. & \\ \hline Digital twin edge network & \(\bullet\) & Efficient communication & Combining digital twins with edge networks, digital twin edge networks (DTFNs) are proposed to fill the gap between physical edge networks and digital systems. & [124] \\  & & & A blockchain-authorized federated learning scheme is proposed to strengthen communication security and data privacy protection in DTFN. & \\  & & & An asynchronous aggregation scheme is proposed, and a digital double reinforcement learning authorization system & \\  & & & is used to schedule relay users and allocate spectrum resources. & \\ \hline Fog computing & \(\bullet\) & Application privacy issues & \(\bullet\) & A new blockchain-based federated learning (FL-block) scheme is proposed to bridge this gap. & [125] \\  & & & F-Block uses the blockchain’s proof-of-every consensus mechanisms so that at autonomous machine learning does not require any centralized permissions to maintain global models and coordinates. & \\  & & & The latency performance of FL-Block is analyzed, and the optimal block generation rate is obtained considering communication, consensus delay, and computing overhead. & \\ \hline Unmanned Aerial Vehicle & \(\bullet\) & Mobile crowd perception & \(\bullet\) & Introduce a blockchain-based cooperative learning architecture for drones to securely exchange local model updates and verify contributions without a central administrator. & [126] \\  & & & By applying local differential privacy a privacy-preserving algorithm is designed to protect the privacy of drones and update the local model with ideal learning accuracy. & \\ \hline Prediction system & \(\bullet\) & Position prediction & \(\bullet\) & Use federated learning to conduct local training on users’ mobile devices while identifying and combuting the possibility of bad actors or adversaries who may intentionally report questionable data to harm the training process. & [127] \\  & & & Use blockchain instead of centralized servers during training to ensure the security of the process. & \\ \hline Traffic flow prediction & \(\bullet\) & Prediction of traffic flow data & \(\bullet\) & Utilize federated learning and blockchain technology for a decentralized approach to ensuring the privacy and security of traffic data. & [128] \\  & & & Aalyze multi-source traffic data for traffic prediction, providing enhanced security for collaborative traffic management. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._With the booming development in modern Information and Communications Technology (ICT), the fourth industrial revolution (Industry 4.0) has overturned the traditional industrial development model. Security requirements in IIoT are generally more stringent than those in typical IoT scenarios [129]. How to protect and use this valuable data in the IIoT to share it in an efficient, secure and cost-effective manner is an urgent issue that needs to be addressed. In the field of the industrial Internet of Things, federated learning based on blockchain provides a BFL framework for multi-party data sharing, which enables devices to safely retrieve data and ensures the accuracy of model training. [PERSON] et al. [22] proposed a secure data transmission method by taking advantage of edge computing and blockchain, using edge servers to improve the efficiency of federated learning, and utilizing the distributed architecture of blockchain to protect data security and privacy. Moreover, [PERSON] et al.