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.

本文提出了基于区块链的联合学习的参考架构，其中一个关键组件是基于去中心化标识符的访问系统，通过执行智能合约来实现访问控制和权限管理，进一步提高了系统的安全性和去中心化性，从而实现了分布式和安全的协作机器学习。

区块链支持的联邦学习：集成DID访问系统的参考架构