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
this https URL

我们设计并开发了一个嵌入以太坊网络的基于区块链的联邦学习框架，该框架提出了一种 FL 与区块链结合的代码实践范例，并且与普通的 FL 训练任务兼容。此外，我们还提出了基于区块链的模型所有权认证架构和模型水印技术来保护模型的知识产权。这些机制在区块链上为联邦学习提供了可验证的训练、聚合和激励分配过程，并将此框架命名为 VeryFL（嵌入区块链的验证联邦学习框架）。