# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 2 Related Work  
The sixth generation network (6G) is expected to bring further evolution to networking, supporting more complex applications such as smart transportation and smart cities. The rapid development of smart industries has led to a data explosion, and transmitting a large number of data samples can result in significant transmission latency [13]. Additionally, standard machine learning methods cannot guarantee user privacy and low-latency transmission. This has led to the introduction of federated learning, a distributed machine learning framework that collaboratively constructs a unified model through local training. The concept of blockchain was initially introduced in the Bitcoin white paper [14]. Fundamentally, a blockchain is a unique form of a linked data structure, where individual blocks are connected to each other through the hash value of the preceding block [15]. As illustrated in Figure 1, each block is composed of two main parts: the block header and the block body. The block header contains information such as the hash of the previous block (previous block Hash), timestamp of submission, mining difficulty (nonce), Merkle root, and the version number of the block. On the other hand, the block body consists of a sequence of transactions that have been sorted. Each transaction includes multiple queries and modification operations related to attributes of business objects [16]. The data among blocks are linked through a chain-like structure to maintain the stability of the blockchain, while the data within each block are connected through a tree-like structure to ensure that transaction data remains unaltered and secure from tampering. Consequently, any attempt to modify the content of the blockchain necessitates substantial computational costs [17]. In the context of federated learning, the model parameter aggregation algorithm federated averaging (FedAvg) is mentioned in reference [18], and benefits from its simplicity and low communication cost, making it the preferred algorithm for federated learning (FL). However, experimental data demonstrate that certain specific distributions of device data can lead to the slow and unstable convergence of model parameters. In reference [5], the concept of distributing training data across mobile devices and learning a shared model through aggregating locally computed updates is introduced as a decentralized approach known as \"federated learning\" (FL). However, considering each client as a trusted entity in this approach leads to malicious participants uploading erroneous model parameters, thereby impacting the performance of the global model. Reference [19] introduces a three-tier collaborative FL architecture to support deep neural network (DNN) training. To address resource allocation issues among participating devices, the authors formulate a stochastic optimization problem to minimize FL evaluation loss. Nonetheless, due to the limited number of clients accessible by edge servers, inevitable training performance loss is encountered. In [20], the blockchain is integrated with FL to address data privacy concerns using blockchain's trust and integrity guarantees. However, the trustworthiness of client devices is not adequately considered, leading to issues of abnormal devices affecting the accuracy of the global model. In another work, presented in reference [21], security and efficiency issues related to node participation in FL are addressed by proposing a blockchain-based secure incentive model. The detection of malicious nodes in this model employs a reputation-based node selection mechanism, which enhances security compared to the aforementioned approaches. Nevertheless, the computation resources required for calculating and verifying trust scores remain substantial. Addressing the aforementioned issues, the proposed ISFL-Sys system establishes a distributed federated learning scenario based on blockchain.