Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (2): 363-371.DOI: 10.11772/j.issn.1001-9081.2020050609

Special Issue: 人工智能

• Artificial intelligence • Previous Articles     Next Articles

Online federated incremental learning algorithm for blockchain

LUO Changyin1,2,3, CHEN Xuebin1,2,3, MA Chundi1, WANG Junyu1,2,3   

  1. 1. School of Science, North China University of Science and Technology, Tangshan Hebei 063210, China;
    2. Hebei Key Laboratory of Data Science and Applications(North China University of Science and Technology), Tangshan Hebei 063210, China;
    3. Tangshan Data Science Laboratory(North China University of Science and Technology), Tangshan Hebei 063210, China
  • Received:2020-05-16 Revised:2020-07-22 Online:2021-02-10 Published:2021-02-27
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61572170, 61170254), the Tangshan Science and Technology Project (18120203A).


罗长银1,2,3, 陈学斌1,2,3, 马春地1, 王君宇1,2,3   

  1. 1. 华北理工大学 理学院, 河北 唐山 063210;
    2. 河北省数据科学与应用重点实验室(华北理工大学), 河北 唐山 063210;
    3. 唐山市数据科学重点实验室(华北理工大学), 河北 唐山 063210
  • 通讯作者: 陈学斌
  • 作者简介:罗长银(1994-),男,陕西安康人,硕士研究生,CCF会员,主要研究方向:数据安全;陈学斌(1970-),男,河北唐山人,教授,博士,CCF高级会员,主要研究方向:数据安全、物联网安全、网络安全;马春地(1999-),男,河北唐山人,主要研究方向:网络安全;王君宇(1996-),女,河北唐山人,硕士研究生,主要研究方向:网络安全。
  • 基金资助:

Abstract: As generalization ability of the out-dated traditional data processing technology is weak, and the technology did not take into account the multi-source data security issues, a blockchain oriented online federated incremental learning algorithm was proposed. Ensemble learning and incremental learning were applied to the framework of federated learning, and stacking ensemble algorithm was used to integrate the local models and the model parameters in model training phase were uploaded to the blockchain with fast synchronization. This made the accuracy of the constructed global model only fall by 1%, while the safety in the stage of training and the stage of storage was improved, so that the costs of the data storage and the transmission of model parameters were reduced, and at the same time, the risk of data leakage caused by model gradient updating was reduced. Experimental results show that the accuracy of the model is over 91.5% and the variance of the model is lower than 10-5, and compared with the traditional integrated data training model, the model has the accuracy slightly reduced, but has the security of data and model improved with the accuracy of the model guaranteed.

Key words: blockchain, ensemble learning, federated learning, incremental learning

摘要: 针对传统数据处理技术存在模型过时、泛化能力减弱以及并未考虑多源数据安全性的问题,提出一种面向区块链的在线联邦增量学习算法。该算法将集成学习与增量学习应用到联邦学习的框架下,使用stacking集成算法来整合多方本地模型,且将模型训练阶段的模型参数上传至区块链并快速同步,使得在建立的全局模型准确率仅下降1%的情况下,模型在训练阶段与存储阶段的安全性均得到了提升,降低了数据存储与模型参数传输的成本,同时也降低了因模型梯度更新造成数据泄漏的风险。实验结果表明,在公开的数据集上进行训练,各时间段内模型的准确度均在91.5%以上,且方差均低于10-5;与传统整合数据训练模型相比,该模型在准确率上略有下降,但能够在保证模型准确率的同时提高数据与模型的安全性。

关键词: 区块链, 集成学习, 联邦学习, 增量学习

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