论文标题
通过可验证的离链计算来推进基于区块链的联合学习
Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations
论文作者
论文摘要
联邦学习可能会受到全球聚集攻击和分布中毒攻击的影响。有人建议将区块链技术以及激励和惩罚机制对抗这些机制。在本文中，我们使用零知识证明来探索可验证的脱链计算，以替代基于区块链的联合学习中的激励和惩罚机制。在我们的解决方案中，学习节点除了计算职责外，还充当链摊贩，提交证明可以证明可以在区块链上验证的参数的计算正确性。我们通过健康监控案例和概念验证实施来证明和评估我们的解决方案，利用Zokrates语言和工具用于基于智能合约的链链模型管理。我们的研究介绍了学习过程的正确性的验证性，从而推进了基于区块链的联合学习。
Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of parameters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.
