## 2 Related Work
### Federated Learning Based on Blockchain  
Traditional federated learning is entirely dependent on the reliability of a central server and is vulnerable to a single point of failure or targeted attack [29]. While hierarchical federated learning has advantages in improving communication efficiency and model performance, it also introduces new security issues and challenges. First, the security risk of middle-tier nodes increases, and if these nodes are controlled by malicious attackers, the aggregation results may be tampered with, leading to bias in the global model [30]. In addition, there is an increased risk of data poisoning and model poisoning attacks, where attackers can maliciously manipulate data or models at the data layer or in the middle layer. Blockchain, due to its decentralized nature, is highly aligned with federated learning, both of which are dedicated to sharing and collaborating on data and computation through a distributed approach. At present, the academic community mainly combines blockchain technology to make the data immutable, limit the central server, and provide trust for federated learning. The combination of these two technologies has obvious advantages, such as having a trust mechanism and data confidentiality. Several studies have used blockchain to detect malicious behavior to enhance system security mechanisms. [PERSON] et al. [31] proposed BFL, a framework that can record local updates and global models through the blockchain to ensure security, and clients would automatically acquire new global parameters through a consensus mechanism. [PERSON] et al. [32] proposed a decentralized public peer-to-peer system called Biscotti that uses blockchain to design a privacy-protecting FL process. [PERSON] et al. [33] proposed the FoolsGold scheme to identify malicious edge nodes, based on the principle that honest edge servers can be separated from malicious edge nodes by the diversity of their gradient updates. [PERSON] et al. [34] proposed a blockchain-based FLchain architecture. FLchain referenced Hyperledger Fabric in its design, creating channels for each global model, each of which maintains a copy of ledger data. [PERSON] et al. [35] proposed the PriModChain framework, which integrates differential privacy and Ethereum smart contracts while ensuring trust and privacy in federated learning.  
Under the federated learning framework based on blockchain, in addition to the need to establish a safe and reliable system, it is also necessary to consider the impact of the limited resources and throughput of the blockchain network on federated learning performance. Therefore, in order to further improve the efficiency of federated learning, some researchers have conducted relevant research. [PERSON] et al. [10] proposed a blockchain-based asynchronous federated learning framework with dynamic scaling factors, proposing new dynamic scaling factors to help improve FL efficiency and accuracy. [PERSON] et al. [36] considered the problem of poor performance of existing blockchain-based federated learning solutions when data are sparse in blockchain-based federated learning clusters, and they proposed CFL, a cross-cluster FL system facilitated by cross-chain technology. CFL connects multiple BFL clusters, where only a few aggregated updates were transmitted over long distances across clusters, thereby improving system efficiency.  
Federated learning architecture based on blockchain technology has become an important direction for the development and research of federated learning. However, blockchain technology only limits the centralized server, and malicious nodes on the client side may also carry out data poisoning or backdoor attacks. In addition to ensuring the security of data, the throughput rate of blockchain itself is limited, which will limit the efficiency of federated learning. However, the current research mainly considers the optimization of the model updating process of federated learning, and it does not consider the optimization of the blockchain mechanism and the system itself.