VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Yihao Li, Yanyi Lai, Chuan Chen, Zibin Zheng

TL;DR
VeryFL is a blockchain-embedded federated learning framework that enhances verifiability, model ownership protection, and incentive mechanisms, addressing reproducibility and trust issues in decentralized FL.
Contribution
The paper introduces VeryFL, a novel decentralized federated learning framework embedded with Ethereum blockchain, enabling verifiable training, model ownership authentication, and incentive distribution.
Findings
Implemented blockchain-based FL algorithms on smart contracts.
Proposed a model ownership authentication architecture.
Demonstrated verifiable training and incentive mechanisms.
Abstract
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Blockchain Technology Applications and Security · Internet Traffic Analysis and Secure E-voting
