# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 1 Introduction  
The sixth-generation network (6G) is considered the forefront of next-generation mobile communication technology and is expected to bring revolutionary breakthroughs in data transmission rates, latency, connection density, and more. Sixth-generation networks still encounter various challenges, including complex network structures and diverse application scenarios [1], all of which require interdisciplinary collaboration to address. 6G will support an even greater number of Internet of Things (IoT) devices, which are often susceptible to security vulnerabilities. IoT devices that are not adequately protected can become entry points for network intrusions, posing a threat to the overall network security. The immutable nature of blockchain technology has provided a novel avenue for enhancing data security. For example, in securities trading, the immutability of historical transaction records can be ensured because each block contains the hash value of the previous block, creating a continuously connected chain. Once transaction data are written to the blockchain, they are almost impossible to modify or delete [2]. Essentially, blockchain is a distinctive chain-like data structure where each block is interconnected through the hash value of its parent block. It can be regarded as a distributed ledger, with each node in the network locally storing a copy of the same ledger, containing an unalterable series of data operation records. The emergence of new applications like the IoT has led to the generation of substantial data by smart devices, driving the advancement of artificial intelligence methods in wireless networks and related applications [3; 4]. Federated learning (FL), as a prominent distributed machine learning framework, enables the training of data analysis models using data from various sources without revealing user data, thereby mitigating certain security risks associated with data sharing [5]. Federated learning allows models to be trained on local devices without the need to transmit raw data to a central server, thus preserving the privacy of user-sensitive data. In contrast to centralized learning methods, federated learning transmits only the updatesto model parameters rather than the entire dataset, reducing the demands on network bandwidth and communication costs. However, during the model parameter aggregation process, some federated learning solutions treat all participants as trusted entities [6; 7], where multiple participants share model updates or hold the same key. This approach can reduce applicability in real-world scenarios and can lead to security issues and privacy breaches [8; 9]. In traditional federated learning, the cloud serves as a parameter server and then parallel model updates based on client-local data and global model aggregation occur on the server side. It is important to note that the communication link between devices (such as smartphones or other terminal devices) and cloud servers is relatively long, resulting in increased data transmission time, which directly impacts the efficiency of model aggregation. Additionally, in relevant scenarios, to prevent malicious clients from affecting the aggregation of global model parameters, cosine similarity is used to measure the trustworthiness of client devices. However, the complex cosine similarity calculation process leads to relatively lower efficiency in model aggregation [10; 11; 12]. In addressing the aforementioned issues, this paper makes the following contributions:  
1. The system ISFL-Sys has been designed, incorporating an incentive mechanism based on blockchain smart contracts and a reliable federated learning mechanism. Encouraging device nodes to participate in the training of model parameters through incentive mechanisms and iteratively updating local model parameters using ETFL federated learning mechanisms to ultimately update global parameters. 2. Designing a lightweight and efficient trust scoring method with improved optimized Euclidean distance similarity metric is included.