## 1 Introduction  
Processing large amounts of data has become a significant challenge for companies, government institutions, universities, and academic groups that need to solve problems efficiently and effectively within a very short timeframe. Artificial intelligence (AI) consists of algorithms that imitate intelligence to solve problems using data collected from different sources of information. This technology allows us to transform the way complex problems are solved, ranging from automation to understanding the various languages with which humans communicate [1]. Artificial neural networks (ANNs) [2], machine learning (ML) [3], fuzzy logic [4], evolutionary computing [5], and natural language processing (NLP) [6] are some of the current applications of artificial intelligence. These applications open up the possibility of helping humans explore or solve challenges that arise in everyday life. Within AI, deep learning (DL) is rapidly gaining reliability in various fields of knowledge, such as computer vision. Many studies have shown that the identification of objects within videos and images provides knowledge for decision-making, imitating the vision of humans [7]. Digital image processing (DIP), through classic methods, allows the application of algorithms to identify patterns or extract characteristics from images or videos. This helps program or develop applications that help people identify certain regions of interest that sometimes go unnoticed. DL, particularly through convolutional neural networks (CNNs), allows us to perform tasks similar to those of DIP but typically with more innovative methods and higher precision than classic DIP methods [8]. Distributed systems (DSs) or high-performance computing (HPC) clusters offer efficient processing of large amounts of data by dividing tasks among multiple computing nodes, thereby reducing time and bottlenecks and effectively increasing resource utilization [9]. DSs play an important role in this research. Their ability to handle large volumes of data and intensive workloads renders them essential in numerous scientific and research fields. An example of these fields is image classification, where this technology fundamentally transforms the techniques and methods implemented in different sectors of society, such as automation, medical diagnostics, precision agriculture, facial recognition, security and surveillance, and autonomous cars, among others. In recent years, there has been a growing interest in the application of DSs to AI. This is due to the need to process large amounts of data efficiently and achieve significantly lower model training times. In the field of image classification, this convergence of DSs and AI is especially relevant since techniques, algorithms, and CNNs are used to identify patterns and objects in images, such as people, plants, stairs, animals, and many other objects. To implement DSs in applied AI with a focus on image classification, distributed deep learning (DDL) tools and frameworks are used. These DDL frameworks play a fundamental role, allowing researchers and developers to harness the power of DSs to train models faster and handle massive datasets, which is essential for high-performance AI applications [10]. To accelerate the processing of AI models, Graphics Processing Units (GPUs) are used. These units are specialized hardware developed to solve complex problems using multi-core technology. This is in contrast to using a conventional computer that only uses Central Processing Units (CPUs) and often requires considerable time to resolve these same problems. In summary, GPUs stand out for their ability to significantly speed up processing compared with conventional CPUs [11]. The synergy between GPUs and DDL frameworks is essential to fully realize the potential of AI, allowing faster processing, efficient management of large amounts of data, and significant advances in research and development in various applications [12].