# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 2 Related Works  
As the number of ENs grows exponentially, traditional centralized FL architecture struggles to maintain stable data interactions and efficient task training due to constraints in communicational and computational resources. The central server often faces malicious threats such as distributed denial of service (DDoS) attacks and inference attacks. To address these challenges, many studies have introduced blockchain technology to replace the C/S architecture with a decentralized distributed architecture. Blockchain [15] is a novel distributed information communication network architecture that offers notable advantages such as decentralization, data immutability, and process traceability. The integration of blockchain and FL provides a viable solution to mitigate dependency on third-party servers. [PERSON] et al. [16] proposed a blockchain-based FL architecture called BlockFL, which employs Proof of Work (PoW) as the consensus mechanism. This approach eliminates single points of failure and trust issues. However, the use of PoW incurs significant additional communication and computational overhead. Furthermore, in EC scenarios with many nodes and different computational capacities, it is impractical to perform aggregation at the edge devices. [PERSON] et al. [17] proposed FLchain based on blockchain, where the blockchain network composed of EC units stores and aggregates data, replacing the central server and reducing the risk of single points of failure. However, this model only registers and verifies node identities through public-private key pairs in dedicated channels, making it difficult to detect and resist malicious behaviors such as data tampering attacks. In FL systems, the heterogeneity of participating nodes has been a significant area of research. Heterogeneity typically refers to the statistical heterogeneity and the differences in local models [6]. In EC scenarios, heterogeneity also includes communication heterogeneity and node state heterogeneity (e.g., computational capacity), aspects that are often overlooked in many studies. Classic FL aggregation algorithms, such as FedAvg [4], randomly select clients to participate in FL training, which can easily incorporate low-quality nodes into the FL task and lacks effective mechanisms to identify and defend against attacks from malicious nodes, reducing the overall efficiency of model aggregation and potentially hindering convergence. To select more reliable nodes in a multidimensional heterogeneous edge network, FL systems often need to design specific node selection strategies. Some works quantify the heterogeneity of local data to prevent the inclusion of low-quality or polluted data models. The AUCTION algorithm proposed by [PERSON] et al. [18] collects local information from clients and inputs it into a reinforcement learning (RL) network to determine the optimal set of clients for each round. [PERSON] et al. [14] proposed Oort, which abstracts node selection as a multi-armed bandit (MAB) problem and designs a RL-based node selection algorithm to improve the accuracy of selecting higher quality data nodes while reducing the number of training rounds in FL. However, these methods do not consider other aspects of heterogeneity over EC scenarios. Some works select nodes with shorter computation and communication delays by detecting the communication quality of the wireless network, thus reducing the waiting time for each round of FL. [PERSON] et al. [13] proposed FedCS, a method to address communication resource heterogeneity. This method uses the time information for model computation, uploading, and aggregation requested from clients, and employs a greedy algorithm to select clients that consume less time. Other works leverage blockchain ledgers to record node reputation information for selection purposes. [PERSON] et al. [19] proposed a decentralized federated learning framework, PP-BFL, based on blockchain.