Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain). The source code is avaliable on https://github.com/GTMLLab/VeryFL.

翻译：区块链赋能的联邦学习近期引发了广泛研究。各类基于区块链的联邦学习算法、架构与机制被设计出来，以解决中心化联邦学习范式带来的单点故障与数据伪造问题。此外，借助区块链更易于向节点分配激励。社区中已涌现出如FedML等各类中心化联邦学习框架，以促进联邦学习研究。然而，去中心化的基于区块链的联邦学习框架仍然缺失，这给研究者基于区块链复现或验证算法性能带来不便。受上述问题启发，我们设计并开发了一种嵌入以太坊网络的基于区块链的联邦学习框架。本报告将呈现该框架的整体结构，它提出了一种联邦学习与区块链结合的代码实践范式，同时兼容常规联邦学习训练任务。除在智能合约上实现部分区块链联邦学习算法以辅助联邦学习训练外，我们还提出一种基于区块链与模型水印的模型所有权认证架构，以保护模型的知识产权。区块链上的这些机制展示了区块链对联邦学习的底层支持，可提供可验证的训练、聚合与激励分配流程，因此我们将该框架命名为VeryFL（嵌入区块链的可验证联邦学习框架）。源代码见https://github.com/GTMLLab/VeryFL。