###### Abstract
In comparison to several traditional machine learning methods, the CNN used in the paper had higher accuracy and efficiency levels [22]. The combination of microbial spectroscopy and deep learning is gradually becoming a trend. Due to the difficulty involved in obtaining a large amount of Raman spectroscopy data from microorganisms, the use of deep learning methods for Raman spectroscopy amplification is also one of the research directions. In 2015, [PERSON] et al. first proposed the concept and implementation framework of generative adversarial networks (GANs) [23], which involve designing a pair of networks: one generates data and the other identifies data, with the two networks competing and learning from each other. After sufficient iterations, the two networks approach Nash equilibrium infinitely, and the generated data is very close to the real data. Over the years, GANs have produced many variants and demonstrated their strong capabilities in various fields. Although the Diffusion model has recently gained popularity and outperformed GANs in image generation [24], as a mature generation model, the latter's status will not be challenged in the short term. The success of generative adversarial networks in image generation has increased researchers' confidence in spectral data amplification. Researchers in the area have adapted GANs and properly set the network structure and parameters to make them suitable for Raman spectroscopic data. [PERSON] used GANs to considerably extend the dataset in an experiment employing Raman spectroscopy to identify three foodborne pathogenic bacteria, eventually obtaining a classification accuracy of 90% [25]. [PERSON] et al. used GANs to gather a large amount of eligible spectral data in a marine pathogen classification experiment, establishing the groundwork for accurate classification [26]. [PERSON] et al. proposed a spectrum recovery conditional generative adversarial network (SRGAN). An SRGAN can accelerate spectral collection and improve the throughput of Raman spectroscopy. The researchers used the SRGAN to process the spectral data of five foodborne bacteria and achieved a classification accuracy of 94.9% in the final classification task. In the comparative experiment, without using the SRGAN, the classification accuracy was only 60.5% [27]. [PERSON] et al. used a PGGAN to amplify spectral data in an experiment classifying five types of marine microorganisms. The results showed that only one-third of the original data needed to be substituted into the model for training to obtain ideal classification results [28]. The above studies demonstrated the efficiency of generative adversarial networks in microbial Raman spectroscopy; however, they have significant limitations. For example, most of them do not evaluate classification accuracy before and after data amplification, making it impossible to determine whether the generative adversarial network plays a role. And if you closely examine the photos associated with the generated data, you will notice that there is still noise in the data. Furthermore, most studies on microbial Raman spectrum classification still use classic machine learning approaches, indicating that there is still space for improvement. To address the aforementioned issues, we proposed a distributed deep learning network based on data enhancement for few-shot Raman spectral classification of shrimp pathogens. The research intentions include the following:  
1. Propose a distributed deep learning network based on data enhancement for few-shot Raman spectral classification of shrimp pathogens. The network is made up of three modules: a Raman spectrum enhancement module (RSEM), a Raman spectrum denoising module (RSDM), and a distributed learning classification module (DLCM). The RSEM controls the enhancement of the spectral data. During the training process, the network in the module employs transfer learning to improve the efficiency and quality of the generated data. 2.