Federated learning is a research hotspot in the field of multi-source privacy data protection. It has advantage that its framework can train a common model that is satisfactory to many parties when the data is not local, but it hardly integrates local model parameters and cannot make full use of multiple sources under safe conditions. Aiming to the problem, this article proposes a deep learning-based federated integration algorithm,which applies deep learning and integrated learning to the framework of federated learning. Under the framework of the proposed federated learning, the parameters of local modes are optimized, accordingly the accuracy of the local model is improved. Besides, by applying variety of integration algorithms to integrate local model parameters, the improvement of model accuracy with taking into account the security of multi-source data simultaneously can be achieved. Experimental comparisons with traditional multi-source data processing technology are demonstrated, and show that the accuracies of the proposed training model on the mnist, digits, letter, and wine data sets are increased by 1%, 8%, -1%, 1%, respectively. So that the proposed algorithm could guarantee the improvement of accuracy and the security of multi-source data and models at the same time, and this has important practical application value.
LUO Changyin, CHEN Xuebin, SONG Shangwen, ZHANG Shufen, LIU Zhiyu
. Federated Ensemble Algorithm Based on Deep Neural Network[J]. Journal of Applied Sciences, 2022
, 40(3)
: 493
-510
.
DOI: 10.3969/j.issn.0255-8297.2022.03.012
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