计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 311-316.doi: 10.11896/jsjkx.191000126
闫凯伦1, 张继连1,2
YAN Kai-lun1, ZHANG Ji-lian1,2
摘要: 机器学习开始在越来越多的行业中得到应用,但使用机器学习执行任务的软件一直受限于第三方软件商更新模型。文中基于区块链,将训练神经网络消耗的算力和区块链的工作量证明机制相结合,提出并实现了模型链。模型链作为一种可用于分享数据和机器学习模型的区块链,基于骨架网络训练神经网络模型,以全网节点匿名分享的数据作为训练模型的数据集,实现了不依赖第三方更新神经网络模型。模型链使用环签名来保护用户数据隐私,节点训练的模型使用统一的测试集评估,通过评估的模型将作为节点的工作量证明用于投票达成一致共识。文中提出了两种可行的激励机制,即物质奖励和模型奖励。对于潜在的威胁,如账本分析、脏数据攻击和欺骗投票,给出了相应的解决方案.实现了一个用于数字识别的模型链。实验结果表明,模型链中的模型可以适应实际场景下发生的用户变迁和数据变化。
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[1] BONAWITZ K,EICHNER H,GRIESKAMP W,et al.Towards federated learning at scale:System design[J].arXiv:1902.01046,2019. [2] LINDEN G,SMITH B,YORK J.Amazon.com recommenda-tions:Item-to-item collaborative filtering[J].IEEE Internet computing,2003 (1):76-80. [3] ZHISONG P,SIQI T,JUNYANG Q,et al.Survey on Online Learning Algorithms[J].Journal of Data Acquisition and Processing,2016,31(6):1067-1082. [4] NAKAMOTO S.Bitcoin:A peer-to-peer electronic cash system[EB/OL].Bitcoin.-URL:https://bitcoin.org/bitcoin.pdf. [5] ZHENG Z,XIE S,DAI H,et al.An overview of blockchain technology:Architecture,consensus,and future trends[C]//2017 IEEE International Congress on Big Data (BigData Congress).IEEE,2017:557-564. [6] KURTULMUS A B,DANIEL K.Trustless machine learning contracts;evaluating and exchanging machine learning models on the ethereum blockchain[J].arXiv:1802.10185,2018. [7] ANDROULAKI E,KARAME G O,ROESCHLIN M,et al.Evaluating user privacy in bitcoin[C]//International Conference on Financial Cryptography and Data Security.Berlin,Heidelberg:Springer,2013:34-51. [8] CONTI M,KUMAR E S,LAL C,et al.A survey on security and privacy issues of bitcoin[J].IEEE Communications Surveys &Tutorials,2018,20(4):3416-3452. [9] RIVEST R L,SHAMIR A,TAUMAN Y.How to leak a secret[C]//International Conference on the Theory and Application of Cryptology and Information Security.Berlin,Heidelberg:Springer,2001:552-565. [10] TIEYAN L,WEI C,TAIFENG W,et al.Machine Learning:Distributed Algorithms,Theory,and Practice[M].Beijing:China Machine Press,2018. [11] SHIZHAO S,WEI CH,JIANG B,et al.Slim-DP:A Multi-Agent System for Communication-Efficient Distributed Deep Learning[C]//Proceeding of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).2018. [12] HAN S,POOL J,TRAN J,et al.Learning both weights andconnections for efficient neural network[C]//Advances in Neural Information Processing Systems.2015:1135-1143. [13] YUAN Y,NI X C,ZENG S,et al.Blockchain consensus algorithms:the state of the art and future trends[J].Acta Automatica Sinica,2018,44(11):2011-2022. [14] DOUCEUR J R.The sybil attack[C]//International workshop on peer-to-peer systems.Berlin,Heidelberg:Springer,2002:251-260. [15] BENET J.Ipfs-content addressed,versioned,p2p file system[J].arXiv:1407.3561,2014. [16] CHEN Y,LI H,LI K,et al.An improved P2P file systemscheme based on IPFS and Blockchain[C]//2017 IEEE International Conference on Big Data (Big Data).IEEE,2017:2652-2657. [17] SCHOENMAKERS B.A simple publicly verifiable secret sharing scheme and its application to electronic voting[C]//Annual International Cryptology Conference.Springer,Berlin,Heidelberg,1999:148-164. [18] SYTA E,JOVANOVIC P,KOGIAS E K,et al.Scalable Bias-Resistant Distributed Randomness[C]//2017 IEEE Symposium on Security and Privacy (SP).IEEE,2017. [19] KIAYIAS A,RUSSELL A,DAVID B,et al.Ouroboros:A pro-vably secure proof-of-stake blockchain protocol[C]//Annual International Cryptology Conference.Springer,Cham,2017:357-388. [20] BONNEAU J,CLARK J,GOLDFEDER S.On Bitcoin as a public randomness source[J].IACR Cryptology ePrint Archive,2015,2015:1015. [21] COHEN B.Incentives build robustness in BitTorrent[C]//Workshop on Economics of Peer-to-Peer Systems.2003:68-72. [22] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [23] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems.2012:1097-1105. |
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