# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Scenarios of Blockchain-Based Federated Learning
[130] presented using blockchain and federated learning to protect IIoT data privacy. The Internet of Things (IoT) ecosystem connects physical devices to the Internet and offers significant advantages in terms of flexibility, responsiveness, and potential environmental benefits [33]. However, the current IoT paradigm relies on centralized storage and computation to operate deep learning algorithms. With the widespread application of blockchain, FL can be enabled in the entire IoT ecosystem. [PERSON] et al. [20] proposed a blockchain-based federated learning platform architecture for fault detection in IIoT to achieve verifiable integrity of client data. In order to solve the problem of heterogeneous data in IIoT fault detection, a centroid distance-weighted federation average (CDW-FedAvg) algorithm is proposed. The model transfer method of federated learning based on blockchain [21] can achieve secure model transfer and accelerate model training on the resource-limited Internet of Things platform, while minimizing the computational cost. Blockchain records interactions between a central server and an IoT device without exposing the device's private data. In order to solve the security and delay problems of the existing power system, [PERSON] et al. [117] proposed a secure and low-delay electromagnetic interference sensing computational off-loading algorithm (BRACE) based on blockchain and semi-distributed learning, which was used to realize the space-air-ground integrated PIoT (SAG-PIoT) and achieve high delay and security performance. More importantly, blockchain technology is a suitable platform to develop decentralized computing systems based on federated learning for military defense without the need for a central entity [125]. Thus, the integration of blockchain and FL is widely utilized in various IoT applications to enhance robustness, security, and privacy. In the field of the Internet of Vehicles belonging to the Internet of Things, blockchain-based federated learning can be widely used. Encryption in the blockchain ensures data consistency, providing secure data transmission and storage. Under these conditions, the blockchain is able to monitor new device entrants through transaction record keeping [131], further guaranteeing data authorization and authentication. Blockchain also ensures secure communication between end devices by storing local training data for various transactions and performing transaction validation on each device [132]. The blockchain-based federated learning vehicle message propagation application proposed by [PERSON] et al. [23] can produce more accurate models in less time, reduce consensus time delay, improve message delivery rate, and more effectively protect the privacy of neighboring vehicles. [PERSON] et al. [24] introduced that blockchain empowers asynchronous federated learning, realizes secure data sharing of the Internet of Vehicles, reduces the transmission load and solves the privacy problems of providers, and ensures the reliability of data sharing. [PERSON] et al. [25] proposed a hierarchical blockchain framework and hierarchical federated learning algorithm for knowledge sharing [133], and vehicles learn environmental data through machine learning methods and share learning knowledge with each other to meet the distributed mode and privacy protection requirements of IoV. [PERSON] et al. [25] proposed a framework for PPioV, based on federated learning (FL) and blockchain technology to protect vehicle privacy in IoV. [PERSON] et al. [64] proposed an autonomous blockchain-based federated learning (BFL) design that integrates blockchain and FL to facilitate efficient communication of autonomous vehicles, and validates on-vehicle machine learning model updates to enable privacy-aware and efficient in-vehicle communication networks. In the healthcare system, [PERSON] et al. [119] conducted an extensive review of recently proposed privacy-preserving technologies to address various privacy concerns arising from the COVID-19 pandemic.