Recently, Blockchain-Enabled Federated Learning (BCFL), an innovative approach that combines the advantages of Federated Learning and Blockchain technology, is receiving great attention. Federated Learning (FL) allows multiple participants to jointly train machine learning models in a decentralized manner while maintaining data privacy and security. This paper proposes a reference architecture for blockchain-enabled federated learning, which enables multiple entities to collaboratively train machine learning models while preserving data privacy and security. A critical component of this architecture is the implementation of a decentralized identifier (DID)-based access system. DID introduces a decentralized, self-sovereign identity (ID) management system that allows participants to manage their IDs independently of central authorities. Within this proposed architecture, participants can authenticate and gain access to the federated learning platform via their DIDs, which are securely stored on the blockchain. The access system administers access control and permissions through the execution of smart contracts, further enhancing the security and decentralization of the system. This approach, integrating blockchain-enabled federated learning with a DID access system, offers a robust solution for collaborative machine learning in a distributed and secure manner. As a result, participants can contribute to global model training while maintaining data privacy and identity control without the need to share local data. These DIDs are stored on the blockchain and the access system uses smart contracts to manage access control and permissions. The source code will be available to the public soon.

翻译：近期，区块链赋能联邦学习作为一种融合联邦学习与区块链技术优势的创新方法，正受到广泛关注。联邦学习允许多个参与者以去中心化方式共同训练机器学习模型，同时保障数据隐私与安全性。本文提出一种区块链赋能联邦学习的参考架构，使多个实体能在保持数据隐私与安全的前提下协作训练机器学习模型。该架构的关键组件是集成基于去中心化身份（DID）的访问系统。DID引入去中心化自主身份管理系统，使参与者能够独立于中心化权威机构管理其身份标识。在该架构中，参与者可通过安全存储于区块链上的DID进行身份验证并访问联邦学习平台。访问系统通过智能合约执行访问控制与权限管理，进一步增强了系统的安全性与去中心化特性。这种将区块链赋能联邦学习与DID访问系统相结合的方法，为分布式安全环境下的协作机器学习提供了稳健解决方案。参与者无需共享本地数据即可参与全局模型训练，同时保持数据隐私与身份自主控制权。这些DID存储于区块链上，访问系统通过智能合约管理访问控制与权限。相关源代码将在近期向公众开放。