人工智能与物联网(Internet of Things,IoT)结合,可以改善物联网中应用的使用体验.在物联网中,数据共享可以改善应用的质量,但是同时也带来了数据安全问题,比如数据在共享过程中存在泄露和无法验证等问题.文中提出一种结合分布式联邦学习和区块链以及具备加密验证的方案,用来保护物联网中共享数据的隐私和数据的有效性.首先,利用联邦学习和区块链将物联网中由直接共享原始数据转化为共享加密的模型参数.接着,提出具备加密验证的方法,在模型聚合阶段对链上参数进行验证和挑选.最后,将所提方法与其他方法进行对比.实验结果表明,所提方法能够有效保证数据隐私并可以实现加密数据的验证,保证最终模型的精度,为物联网中数据高质量共享提供保障.
Study on Cryptographic Verification of Distributed Federated Learning for Internet of Things
Artificial intelligence is combined with the Internet of Things(IoT)to enhance application usage.In the IoT,data sha-ring can improve the quality of applications,but it also brings data security issues,such as data leakage and inability to verify du-ring the sharing process.This paper proposes a scheme combining distributed federated learning with blockchain and encryption verification to protect the privacy and effectiveness of shared data in the IoT.First,federated learning and blockchain are used to transform the direct sharing of original data into shared encryption model parameters in the IoT.Next,this paper proposes a method with encryption verification to verify and select on chain parameters during the model aggregation stage.Finally,the pro-posed method is compared with other methods,and experimental results show that our method can effectively ensure data privacy and achieve verification of encrypted data,ensuring the accuracy of the final model,and providing guarantees for high-quality data sharing in the IoT.
Federated learningBlockchainInternet of ThingsCryptographic verificationHomomorphic encryption
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