# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 1 Introduction
This heterogeneity is reflected not only in differences in local data quality and feature distribution--where node data are neither independently nor identically distributed--but also in variations in local computing power, real-time network communication quality and other factors. These disparities introduce several vulnerabilities to the FL system. For example, data heterogeneity can cause inconsistent model updates, as nodes with skewed data distributions may contribute biased gradients, thereby degrading the global model performance [4]. Similarly, communication heterogeneity, such as poor communication quality or high latency, can lead to inefficient data transmission and delayed model updates, further affecting the overall training process [7]. In short, these forms of heterogeneity significantly complicate the identification of malicious or low-quality ENs, leading to prolonged training times and reduced effectiveness of the global model. Lastly, while communication between the server and edge devices replaces raw data with model parameters, this approach is not without risks. Threats such as model inversion attacks, model backpropagation attacks, and gradient leakage still pose significant security concerns. Adversaries may infer original data by analyzing the shared model parameters,compromising user privacy and revealing sensitive information [8]. These vulnerabilities have been widely studied in the context of federated learning and raise significant concerns about potential breaches of data privacy and integrity [9; 10]. Some studies have introduced blockchain to address the issue of traditional FL systems' reliance on a central server. However, these approaches often face challenges such as high computational costs of consensus algorithms and limited adaptability to EC scenarios. Regarding the node heterogeneity problem in EC, existing methods primarily focus on optimizing data heterogeneity across distributed edge nodes or quantifying and selecting nodes based on single dimensions, such as communication quality or node credibility. However, these approaches fail to effectively address the multi-dimensional heterogeneity faced by FL systems in real-world EC environments. Moreover, malicious attacks, such as model poisoning and delay deception attacks [11; 12], may bypass existing prior detection mechanisms and undermine the security and reliability of the system. To date, there is a lack of research that simultaneously considers both the multi-dimensional heterogeneity of nodes and the countermeasures to these security threats, resulting in the limited robustness and adaptability of current methods in practical applications. In light of the aforementioned security challenges faced by traditional FL in cross-zone data interaction scenarios, this paper proposes a novel federated learning method with blockchain and a credibility mechanism (named CS-FL). The primary contributions of this paper are summarized as follows:  
1. This paper proposes a blockchain-based dual-layer FL framework for EC networks. A blockchain is established between ZSs to replace the central server, and a blockchain ledger is introduced. The block structure is designed to provide multi-index storage and update functions, while the PBFT consensus mechanism enhances the security of the blockchain network and the robustness of the FL system. 2. Several measurement indexes are defined to evaluate the local quality, communication resources, and credibility of ENs, considering the multi-dimensional heterogeneity of nodes in EC scenarios. These indexes aim to comprehensively quantify the real-time status and historical credibility of ENs. 3. A three-phase FL training process is designed, with the credibility mechanism as the core. Specifically, a lightweight probe task is used to perform prior assessment of the node status, reflected in updates to indexes. The credibility indexes are then used to select nodes based on a greedy algorithm.