  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.

Machine Learning;Cryptography and Security