# Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment
## 2 Related Work  
A centralized network topology has more serious system security issues and higher communication overhead compared with a decentralized distributed network topology. Traditional federated learning adopts a centralized topology with a single centralized server responsible for aggregating the global model. As federated learning takes hold in real-life production, the shortcomings of the centralized topology model have gradually become apparent [17]. In practical applications, the centralized server will put huge pressure on the network bandwidth overhead and also reduce the robustness of the system, which will affect the model training process of federation learning once the server is maliciously compromised. Blockchain is a distributed technique with decentralized characteristics and incentive mechanism [5; 18; 19], which can effectively solve the above problems of traditional federated learning. In addition, due to being tamper-proof and anonymous, blockchain can guarantee data security. Therefore, a lot of work has been done on decentralized federated learning methods using blockchain. However, decentralized federated learning with blockchain still faces the challenge of model training efficiency. [PERSON] et al. [20] proposed a blockchain-based federated learning architecture. The local device in this architecture updates the local model based on its available data samples. The architecture uses blockchain to reward updates from local devices, and the reward is proportional to the number of local data samples. This simple reward scheme is not able to accurately reflect the magnitude of the contribution made by the local device to the global model training. [PERSON] et al. [21] designed a federated learning scheme incorporating blockchain incentives. The scheme ensures system reliability by rewarding honest local devices and punishing dishonest ones. They also introduced a consensus protocol based on a committee mechanism, which participates in the consensus process by randomly selecting nodes to form a committee. However, randomly selecting committee nodes is almost negligibly close to a completely random scheme and is probably not optimal. Local devices make significant contributions to the training of the federated learning model, so it is important to reasonably reward local devices. Existing federated learning incentive schemes generally agree that local devices should be fairly rewarded based on the magnitude of their contribution to the model. [PERSON] et al. [22] stated that the most widely used scheme to evaluate the contribution size of local devices is Shapley values (SVs). SVs can fairly distribute rewards for model training, and it is widely used in many fields, such as economics, information theory, and machine learning. However, SV-based reward schemes usually require exponential time to compute, and the computational cost is prohibitive. In the edge computing environment, the matching problem between local devices and edge servers has an important impact on the data transmission delay and the overall system efficiency. [PERSON] et al. [23] proposed a matching method for mobile edge computing and device-to-device communication environments. This method uses a game model to solve the offloading problem of local devices. Each local device is regarded as a gamer, and the offloading strategy is obtained through a mutual game to make the system reach Nash equilibrium. [PERSON] [24] proposed an intelligent scheduling matching scheme based on a delayed acceptance algorithm. It combines the Hopfield neural network and the decision tree model and quantifies the assignment scheduling problem into a matching optimization problem by defining the cost coefficient between tasks and equipment. [PERSON] et al. [25] proposed an asynchronous greedy matching algorithm, which builds a preference list of both parties based on the utility value between the cooperative node and the requesting node, and uses the greedy strategy for stable matching.