# Blockchain-Based Federated Learning: A Survey and New Perspectives
###### Abstract
Pervasiveness and ubiquity of blockchain in federated learning systems.In practical application scenarios, blockchain federated learning has been widely used in many fields. In the industrial Internet of Things (IIoT), the platform architecture of blockchain-based federated learning is used for failure detection in the IIoT for verifying the integrity of client data [20], safe model transfer and accelerated model training [21], and ensuring the security of industrial data transmission [22]. In the field of car networking, BFL can be used for promoting the dissemination of vehicle network information, which overcomes problems such as low data reception rate and privacy security [23; 24]. Using a layered blockchain framework is proposed to enable vehicles to learn environmental data through machine learning methods and share learning knowledge with each other [25], addressing the privacy concerns of private data sharing [26]. In medical system networks, BFL is applied for privacy protection of electronic health records [27; 28], sensitive healthcare data privacy protection in clinical institutions [29], and detecting COVID-19 with CT imaging [30]. _What are the differences between this survey and former ones?_ In recent years, the number of research publications on blockchain-based federated learning has grown exponentially, strongly demonstrating the importance and ubiquity of blockchain in federated learning research, making this study more persuasive. However, to the best of our knowledge, few systematic reviews have sufficiently summarized the field, pinpointing existing work and current progress. We conduct some surveys on traditional federated learning and blockchain technology and find that many papers lack a comprehensive and systematic review of blockchain-based federated learning. Although two papers are important to the field, they suffer from the disadvantages of few references and insufficient content [31; 32]. Also, [PERSON] et al. [31] highlight that there is neither an in-depth exploration of this area nor a comprehensive description of solutions to federated learning challenges from a blockchain perspective. More importantly, [PERSON] et al. [32] have less description of the blockchain-based federated learning model and lack a systematic description of the combination of the two technologies. Lastly, Internet of Things (IoT) security and digital twins are the main application directions in [33; 34]. Although there exists research on specific models, there is a lack of investigation in other application fields with strong limitations. Contributions of this survey.The purpose of this survey is to comprehensively review the research progress of blockchain-based federated learning. Although they are effective for preserving privacy, federated learning systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into federated learning systems to provide stronger security, fairness, and scalability [35]. On the other hand, this review summarizes the technical framework integrating the two, and summarizes the application scenarios in healthcare [36; 37], finance [38], industry [39], and so on. This review lays the foundation for promoting innovation in the field of federated learning and tapping into the richness of this research field. This review serves researchers, practitioners, and educators interested in federated learning, hoping that they will have a general guideline when choosing a blockchain to solve the federated learning task at hand.