# 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
#### 4.2.3 Privacy Mechanisms  
Privacy mechanisms refer to participant authentication and node data storage, as well as distributed storage of data. The blockchain provides three mechanisms--identity authentication, node data confidentiality, and data storage--to achieve privacy protection in BFL. Therefore, blockchain-based federated learning has a strong privacy protection system. Blockchain provides an identity authentication mechanism for on-chain data. Previously, federated learning clients did not require authentication to upload model updates, which was risky. Now, through the blockchain's authorization mechanism and identity management, untrusted users can be integrated as participants to establish a secure and trusted cooperation mechanism. [PERSON] et al. [96] introduced a model update chain to secure local model updates and global model updates by utilizing a directed acyclic graph (DAG) blockchain. Blockchain provides a confidentiality mechanism for node data. In federated learning, although all participants exchange gradient information and do not expose their raw data to the outside world, there is still a risk of data inversion by opening the data gradient update process. Conversely, a blockchain-based federated learning framework avoids the privacy risks of centralized structures. [PERSON] et al. [18] proposed a cryptography gradient audit method using two blockchains, which simultaneously realized privacy-preserving gradient recording and trusted quality audit. Blockchain provides a data storage mechanism. Blockchain is essentially a distributed public ledger that connects blocks into a chain, which is actually a peer-to-peer accounting system on which every node can record information. The digital records on the blockchain cannot be tampered with or forged, which improves the efficiency of data flow and forms a new storage model. The model parameters of federated learning are stored in the blockchain, which not only ensures the safety and reliability of model parameters, but also promotes the contribution of data by participants. Frankly speaking, the superiority of blockchain-based federated learning in data storage and even data encryption has been significantly improved. In addition, many BFL-based blockchain technologies cannot completely prevent privacy leaks, so it is best to incorporate differential privacy, homomorphic encryption, and other technologies with BFL. Homomorphic encryption [94] improves the security of node storage and computation in federated learning. It is important to process personal data by effectively encrypting it before it is shared on the blockchain. [PERSON] et al. [105] proposed a blockchain-based privacy-preserving federated learning scheme called BPFL, which combines homomorphic encryption and Multi-Krum technology to achieve ciphertext-level model aggregation and model filtering. In addition, [PERSON] et al. [106] proposed an explainable FL (DP-BFL) framework based on differential privacy blockchain for data privacy. [PERSON] et al. [107] integrate the blockchain's FL with the Wasserstein Generative Adversarial Network (WGAN) of Differential Privacy (DP) to protect the model parameters of edge devices in the Beyond-5G (B5G) network.