# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 1 Introduction  
With the rapid advancement of 5G technology and artificial intelligence, the scale of the Industrial Internet is expanding exponentially, leading to a significant increase in the number of terminal devices deployed at the network edge. These devices are often distributed across multiple regional clusters, influenced by factors such as enterprise structure, departmental boundaries, and industrial chain positioning. Meanwhile, the use of these devices generates substantial amounts of local data. Edge computing (EC) [1] has emerged as a new paradigm in modern computing architectures. By deploying a zone server (ZS) at the location where data are generated by edge terminal devices, EC technology can process data from these devices within its designated service area. ZS enables EC to efficiently serve a large number of nearby edge nodes (ENs), thereby alleviating the pressureon centralized data collection and computation systems. Consequently, it significantly reduces the response latency of the overall service. As we enter the era of big data, the value of data are increasingly recognized not only in their local utilization but also in their potential to uncover greater insights through interaction and aggregation. However, data generated on EN often contain sensitive user information, presenting significant challenges, such as bandwidth constraints, latency, and the protection of user privacy, when using traditional cloud computing models [2]. Taking the railway industry as an example, with the continuous improvement of information technology, various departments and business lines within the railway sector have accumulated vast amounts of data. These data are multi-source, heterogeneous, large in volume, and extremely valuable. Comprehensive management and in-depth analysis of this data can not only fully realize its value but also improve the efficiency of rail transportation production and reduce transportation costs. However, due to the confidential nature of the data across departments, direct aggregation and processing are restricted [3]. Federated learning (FL) [4] has emerged as an effective solution to reconcile the contradiction between safeguarding data privacy and realizing the value of data sharing. FL facilitates the sharing of knowledge among multiple participants without the need for direct exchange of raw data, enabling the training of machine learning models across users who maintain local datasets. The integration of FL in cross-zone EC scenarios allows a large number of terminal devices to process data locally, thereby eliminating the need to store or transfer original data to a centralized server. This approach not only enhances user privacy and data security but also reduces network bandwidth usage. Furthermore, processing data at the edge of the network reduces reliance on the central server's processing capacity and minimizes training delays [5]. Consequently, the adoption of FL significantly contributes to privacy protection and data security in scenarios involving cross-zone data interaction for enterprises. However, FL systems deployed over EC scenarios also encounter notable security challenges. Firstly, in the traditional FL client/server (C/S) architecture, FL tasks rely on a central server to coordinate activities across various zone servers. This central server is responsible for receiving model parameter updates from edge servers and distributing the aggregated global model. However, the presence of a central server inherently establishes a centralized network structure, making the system vulnerable to saturated network attacks that could potentially cripple the entire system. Moreover, as the number of edge nodes increases, the central server may face limitations in processing and storage capacity. Secondly, the heterogeneity [6] of ENs within the multi-access EC network presents significant challenges to FL.