# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 6 Conclusions  
This paper proposes a blockchain-based cross-zone federated learning approach to address network edge threats, enhancing the security and stability of FL systems. The Zone-Chain framework introduces a decentralized blockchain network and ledger between zone servers, ensuring transparent and secure data exchange and model training. A node selection strategy based on a credibility mechanism and hybrid encryption algorithm uses key indexes such as data quality, communication resources, and node credibility to select higher-quality nodes, strengthening the FL system's protection and fault tolerance. Experimental results demonstrate that CS-FL excels in defending against various threats, such as local poisoning, communication delays, and malicious behaviors, outperforming existing methods like Oort and FedCS, particularly in model poisoning and channel deception attacks. This performance is attributed to the SCI Incentive Mechanism, which provides secondary evaluation of node credibility. In practical applications, CS-FL offers a secure solution for multi-source sensitive data exchange and collaborative training, particularly in Industrial Internet scenarios, such as the railway industry. Furthermore, CS-FL enables edge devices to flexibly respond to network attacks, ensuring stability and security during training. This lays a solid foundation for the future deployment of FL in fields like intelligent transportation and smart manufacturing. This study has several limitations. First, while the selected evaluation dimensions are representative, they do not fully capture the range of threats in real-world EC scenarios. Second, the penalty indices (e.g., \(P_{t}\) and \(P_{b}\)) and threshold settings in the node selection strategy (e.g., \(\varepsilon\)) rely on empirical results and lack a dynamic adjustment mechanism, limiting adaptability to changing environments. Third, the anomaly detection in the SCI strategy excludes all deviations from the mean, which may misclassify updates from normal nodes under highly heterogeneous data conditions. While simple and robust against attacks, this may lead to false positives. These limitations may affect CS-FL's performance in more complex, dynamic scenarios. Future work will focus on improving dynamic adjustment for penalty indices and threshold settings in the node selection strategy, allowing real-time adaptation based on node state changes. The research will also introduce additional metrics, such as node mobility and device types, to more comprehensively assess node states. With these improvements, CS-FL will support a broader range of device types, dispersed nodes, and heterogeneous data in future applications, ensuring system security and stability. These advancements will significantly promote the deployment of FL in real-world EC networks. Figure 11: The accuracy of the FL task under complicated scenarios on two datasets. (**a**) The accuracy of models with the epoch of FL on the MNIST dataset. (**b**) The accuracy of models with the epoch of FL on the CIFAR-10 dataset. **Author Contributions:** Conceptualization, C.Z. ; Methodology, C.Z. and H.S. ; Validation, D.W.; Visualization, H.S. and Z.S. ; Writing--original draft, C.Z. ; Writing--review and editing, H.S. and Z.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the science and technology project of the Shuohuang Railway Development Co., Ltd., National Energy Group \"Research on secure data interaction and digital security management of devices based on blockchain technology\" (SHTL-23-30). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** Author [PERSON] was employed by the company Shuohuang Railway Development Co., Ltd., National Energy Group.