中圖分類號：TP311 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.223244
中文引用格式： 原野，田園，蔣七兵. 一種深度神經網絡的分布式訓練方法[J]. 電子技術應用，2023，49(3)：48-53.
英文引用格式： Yuan Ye，Tian Yuan，Jiang Qibing. Distributed training method for deep neural networks[J]. Application of Electronic Technique，2023，49(3)：48-53.

Distributed training method for deep neural networks

Abstract： Abstract： Deep neural networks have achieved great success in classification and prediction of high-dimensional data. Training deep neural networks is a data-intensive task, which needs to collect large-scale data from multiple data sources. These data usually contain sensitive information, which makes the training process of convolutional neural networks easy to leak data privacy. Aiming at the problems of data privacy and communication cost in the training process, this paper proposes a distributed training method for deep neural networks, which allows to jointly learn deep neural networks based on multiple data sources. Firstly, a distributed training architecture is proposed, which is composed of one computing center and multiple agents. Secondly, a distributed training algorithm based on multiple data sources is proposed, which allows to distributed jointly train models through the splitting of convolutional neural networks under the constraints that raw data are not shared directly and the communication cost is reduced. Thirdly, the correctness of the algorithm is analyzed. Finally, the experimental results show that our method is effective.

Key words : deep neural network；distributed training；supervised learning；privacy guarantee