Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.

翻译：联邦学习是一种去中心化的机器学习范式，允许多个客户端通过利用本地计算能力和模型传输进行协作。该方法通过将训练数据分布到异构设备上，降低了集中式机器学习方法的成本和隐私风险，同时确保数据隐私。另一方面，联邦学习在存储、传输和共享过程中因缺乏隐私保护机制而存在数据泄露的缺陷，从而给数据所有者和提供者带来重大风险。区块链技术作为联邦学习中提供安全数据共享平台的一种有前景的技术，尤其是在工业物联网（IIoT）环境中。本综述旨在比较基于区块链的联邦学习架构中采用的各种数据隐私机制的性能和安全性。我们对现有关于区块链技术为联邦学习提供的安全数据共享平台的文献进行了系统回顾，深入概述了基于区块链的联邦学习、其核心组成部分，并讨论了其原理和潜在应用。本综述论文的主要贡献是识别关键研究问题，并为基于区块链的联邦学习的未来研究提出潜在方向。