# A Survey on Blockchain-Based Federated Learning
## 1 Introduction  
Currently, artificial intelligence (AI) technology is advancing rapidly, transitioning from its invention phase a few years ago to the phase of practical application. AI technology is being applied in an increasing number of scenarios. However, as algorithms and computing power have significantly improved, there is a growing demand for larger datasets and increased emphasis on data privacy protection. How to meet the data requirements of AI models in this context has become an urgent challenge in the development of AI technology today. Presently, machine learning (ML) algorithms increasingly rely on vast amounts of data. However, the reality is that, due to privacy constraints, data are scattered across different organizations. Therefore, the current development of AI faces two challenges: the data silo problem and issues related to data security and privacy. Firstly, the data silo problem greatly limits the availability of big data. Despite the internet generating billions of data daily, there is a lack of useful, high-dimensional, high-quality data. Secondly, countries worldwide are strengthening their data security protection and privacy. Strict regulations on user data privacy and security management are becoming a global trend. Without providing users with reliable privacy protection methods, the issue of data insufficiency will severely restrict the development of AI. Due to these factors, the emerging ML technique, federated learning (FL) [1; 2], has become a popular research topic in the field of ML. The main idea of FL is to enable a large number of user devices that store data locally (referred to as clients) to collaboratively train a single ML model without the need to share their raw data. For example, datafrom different hospitals are often isolated, creating data silos. Because each data silo has limitations in terms of data size and approximation to the actual distribution, a single hospital may struggle to train and attain high-quality models with high prediction accuracy for specific tasks. Ideally, if multiple hospitals could collaborate and combine their data to train ML models collectively, more accurate training results could be achieved. However, due to various policies and regulations, data cannot be easily shared between hospitals. Similarly, the data silo phenomenon is prevalent in many other fields, including finance, government, and supply chains. Additionally, policies like the general data protection regulation (GDPR) [3] set rules for data sharing between different organizations. As a result, developing an FL method that can deliver excellent prediction accuracy while adhering to policies and regulations to protect privacy is a highly challenging endeavor. In addition, FL also has its unique set of challenges. Primarily, the paradigm often necessitates the involvement of a considerable number of users with diverse cultural backgrounds and intricate behavioral patterns, complicating mutual trust and augmenting the risk of inadvertent privacy breaches for honest participants [4; 5; 6; 7; 8]. FL protects users' sensitive data by keeping the source data local and only exchanging model updates, such as gradient information. However, research indicates that gradient information can leak users' private data [9; 10; 11; 12; 13; 14]. Attackers can indirectly infer label information and dataset membership information through the gradient information uploaded by clients. [PERSON] et al. [13] extracted sensitive user data, such as specific bank card numbers, from a recursive neural network trained on users' language data. [PERSON] et al. [10] investigated how to steal data privacy from model information and conducted inversion attacks on linear regression models through dosage prediction experiments, obtaining sensitive patient information. [PERSON] et al. [12] launched attacks on model aggregation using generative adversarial networks (GANs). The experimental results show that malicious clients can steal users' data privacy by generating similar local model updates. [PERSON] et al.