This paper presents an innovative reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology. This results in a decentralized, collaborative machine learning system that respects data privacy and user-controlled identity. Our architecture strategically employs a decentralized identifier (DID)-based authentication system, allowing participants to authenticate and then gain access to the federated learning platform securely using their self-sovereign DIDs, which are recorded on the blockchain. Ensuring robust security and efficient decentralization through the execution of smart contracts is a key aspect of our approach. Moreover, our BCFL reference architecture provides significant extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. Participants can authenticate and then gain access to the federated learning platform securely using their self-sovereign DIDs, which are securely recorded on the blockchain. The pivotal contribution of this study is the successful implementation and validation of a realistic BCFL reference architecture, marking a significant milestone in the field. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community. This research not only bridges a crucial gap in the current literature but also lays a solid foundation for future explorations in the realm of BCFL.

翻译：本文提出了一种面向区块链赋能联邦学习（BCFL）的创新参考架构，该架构融合了联邦学习与区块链技术的优势，构建了一个去中心化、协作式的机器学习系统，在保护数据隐私的同时支持用户自主管理身份。该架构创新性地采用基于去中心化标识符（DID）的认证系统，使参与者能够通过区块链上记录的自主权DID安全地完成身份验证并接入联邦学习平台。通过智能合约确保系统安全性与高效去中心化是本方案的核心特征。此外，BCFL参考架构具备显著的可扩展性，可根据具体需求与应用场景灵活集成各类附加模块，从而为多样化的BCFL应用提供适应性解决方案。参与者利用安全记录在区块链上的自主权DID完成身份验证后，即可获得联邦学习平台的接入权限。本研究的关键贡献在于成功实现并验证了贴近实际场景的BCFL参考架构，标志着该领域的重要里程碑。我们将于近期公开源代码，以促进社区内的深度创新与适应性开发。这项研究不仅填补了当前文献的关键空白，更为BCFL领域的未来探索奠定了坚实基础。