# Blockchain-Based Federated Learning: A Survey and New Perspectives
###### Abstract  
Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field. blockchain; federated learning; blockchain-based federated learning; distributed machine learning +
Footnote †: journal: Medical Imaging  
fere with model training. In addition, federated learning is vulnerable to malicious servers, and huge threats are triggered by information leakage [4]. Meanwhile, the centralized parameter server is vulnerable to a single point of failure and can cost all trainers. Furthermore, the problem of insufficient incentive and lack of a certain reward distribution system for federated learning model training also needs to be solved. Therefore, it is critical to establish an open, auditable [5; 6], decentralized [7], incentivized, and defensible [8] federated learning mechanism. Blockchain, as the underlying technology underpinning Bitcoin, is essentially a decentralized database [9] capable of building ledgers in a secure and verifiable manner [10]. Moreover, consensus mechanisms, cryptography and smart contracts in blockchain enable secure transactions between participants without a central authority. The structure of blockchain is shown in Figure 1. Because of the advanced characteristics of blockchain, such as anonymity, tamper resistance, and decentralization, blockchain is widely used in fields such as in-vehicle networks [11; 12], industrial Internet of Things [13; 14], and medical networks [15]. More importantly, blockchain-distributed trust provides a new approach for designing federated learning frameworks and paradigms. The main feature of blockchain is that it allows untrusted participants to communicate with each other and send status update messages in a secure manner without the involvement of a fully trusted third party or authorized central node. Therefore, blockchain can effectively solve the problems in federated learning, an approach called blockchain-based federated learning (BFL). Blockchain's central aggregator identifies malicious and unreliable actors by automating smart contract execution to defend against federated learning poisoning attacks [16; 17]. [PERSON] et al. [18] designed a new blockchain platform to manage reputation value in a decentralized manner, which can ensure accurate historical reputation, thus greatly improving the accuracy of FL. [PERSON] et al. [19] proposed a blockchain-based encryption gradient audit method that uses the behavior chain to record the encryption gradient from the data owner to improve the security of FL. A blockchain-assisted federated learning (BC-FL) framework was developed to overcome the single point of failure caused by the FL central server. Figure 1: Blockchain architecture.