A blockchain-enabled learning model based on distributed deep learning architecture

【Author】 Zhang, Yang; Liang, Yongquan; Jia, Bin; Wang, Pinxiang; Zhang, Xiaosong

【Source】INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS

【影响因子】8.993

【Abstract】Aiming to address the unsatisfactory performance of existing distributed deep learning architectures, such as poor accuracy, slow network communication, low arithmetic speed, and insufficient security, we propose and design a learning model based on a distributed deep learning and blockchain architecture. We use a hybrid parallel algorithm based on blockchain (HP-B) to build a distributed deep consensus learning model. The HP-B algorithm is grouped according to the performance of computing nodes participating in training, network links and training samples, and the grouped computing equipment performs optimal distributed computing. The purpose of this approach is to solve the security and scalability concerns and improve the convergence speed and accuracy of deep learning. The proposed method achieves good results on the CIFAR-100, CIFAR-10, and IMAGENET data sets. Finally, the distributed deep learning model based on blockchain is combined with the generative adversarial network to solve the segmentation problem of medical imaging data, and the experimental results are superior to those of other networks.

【Keywords】blockchain; distributed deep learning; generative adversarial networks; hybrid parallel algorithm; image segmentation

【发表时间】

【收录时间】2022-06-15

【文献类型】实证性文章

【主题类别】

区块链技术-协同技术-机器学习