# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Direction of Blockchain-Enabled Federated Learning
#### 4.2.1 Incentive Mechanisms  
Incentive mechanisms refer to the motivation of participants to participate in training tasks and the system of rational distribution of rewards. The goal of the incentive mechanism is to create and distribute value. Under the premise of sufficient incentive, participants are rewarded or punished according to their own contribution. The introduction of incentives is crucial to federated learning [97]. For those who do not have enough data to train the model themselves, using incentive mechanisms can motivate local data to participate in collaborative training, and the accuracy of model training will be greatly improved. Secondly, a reasonable reward and punishment system can ensure the honest behavior of participants in model training and gradient transactions and improve the application level of the model.  
Fundamentally speaking, the blockchain is open and transparent. After the training of the federated learning model is completed, it can use smart contracts and consensus algorithm technology to allocate reward resources according to the quantity and quality of training data provided by each participant, and reward resources are written into the blockchain to bring in more participants and improve the cooperation of participants.  
Existing studies on incentive mechanisms in federated learning mainly focus on one dimension and have some limitations [98]. For instance, [PERSON] et al. [99] only considered the size of training data. Nevertheless, in blockchain-based federated learning, related problems are solved. For example, [PERSON] et al. [100] took reputation as a fair indicator to select reliable employees, and employees with good reputation could obtain more rewards in model training. [PERSON] et al. [101] proposed an incentive mechanism based on contract theory aimed at obtaining hidden type information from users and maintaining long-term participation in healthcare applications. [PERSON] et al. [57] presented a blockchain-based federated learning payment system, where the contribution of each FL participant is evaluated through the proof of the Shapley (PoSap) consensus protocol.  
In particular, in order to ensure the transparency of the incentive mechanism and the practice of FL, [PERSON] et al. [29] proposed that an incentive mechanism based on contribution points aims to fairly reward FL participants for contributing their local data, realize the authenticity of FL model aggregation and provide FL participants with the effectiveness of incentives. [PERSON] et al. [102] provided an incentive mechanism based on Bayesian game theory to solve challenges in UAV-assisted wireless networks. [PERSON] et al. [58] proposed a blockchain-based federated learning framework and protocol to transparently evaluate the contribution of each participant. The framework protects the privacy of all parties during the model building phase and transparently evaluates contributions based on model updates.  
In addition, blockchain can also provide reasonable incentives and misconduct deterrence for collaborative models to facilitate alliance between customers [31]. If the local model update is successfully validated, the local device can be rewarded [79; 80]. If the uploaded update is incorrect, the local device will also be penalized [76].