# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Frameworks of BFL  
This section summarizes and compares the blockchain-based federated learning (BFL) frameworks proposed in the literature collected for this article, and analyzes their different design approaches. Figure 4 shows the schematic diagram of the BFL framework classification summarized in this article. First, a traditional FL framework usually consists of a central server and multiple users (or devices/clients). Early typical BFL frameworks generally used decentralized blockchains to replace the central server in traditional FL frameworks. The main purpose was to address the problems of single-point trust and failures caused by the central server [80; 81; 82; 83]. An example of this type of framework is shown in Figure 5. Users submit their local models to the miners maintaining the blockchain. The miners carry out cross-validation, model aggregation, and other steps, and produce a consistent global model based on the consensus mechanism. They then use blocks to store and propagate this global model. Users can download the consistent global model from the block to their local devices for the next round of training. In addition to using blockchains to replace the central server, this kind of typical framework usually has two features. Before model aggregation, by introducing mechanisms such as cross-validation, it ensures that the local models participating in the global model update conform to the direction of the global model update, preventing users from using malicious models to jeopardize the security of the global model. Furthermore, by introducing a reward mechanism, users can be incentivized to contribute high-quality data and actively participate in training, effectively alleviating the fairness problem of FL. This prevents users with different contributions from receiving similar rewards, in case of users slacking off. On the basis of this typical framework, some BFL frameworks have led to further innovation in areas such as consensus mechanisms and reward mechanisms. The BFL framework with a committee consensus framework proposed in [30] does not adopt the commonly used PoW consensus mechanism, but instead proposes the committee consensus mechanism (CCM). A feature of this mechanism is the use of a committee composed of some honest nodes to carry out local gradient verification of the model and block  
Figure 4: Types of BFL frameworks. Figure 5: A generalized BFL paradigm. generation. Because only a subset of nodes participate in local model verification and global model updates, the overall efficiency of FL is significantly improved. This mechanism requires nodes outside the committee to send their local models to committee nodes for verification and scoring, and only allows qualified models to participate in global model updates. By this mechanism, to enhance security, members of the committee are periodically replaced based on node historical performance scores and smart contracts. [PERSON] et al. [84] introduced deep reinforcement learning to find the optimal system parameters that can minimize system delay, energy consumption, and total rewards, including recommended data volume and energy consumption when users train local models, as well as block generation rate. The FL, via the MEC-enabled blockchain network (FLChain) framework proposed in [85], includes both mobile devices and edge devices. Mobile devices primarily update local models using data samples on the devices, while edge devices provide more abundant network resources for resource-limited mobile devices and also act as nodes in the FLChain network to maintain the blockchain. FLChain utilizes the channel technology in the consortium blockchain Hyperledger Fabric, leveraging the isolation feature of the channels to enhance the security of global model training and provide a certain degree of data privacy preserving. [PERSON] et al. [86] proposed a crowdsourcing BFL framework called the crowd computing secure framework based on blockchain technology and FL (CrowdSFL).