## 1 Introduction  
One important task in learning analytics (LA) [1] and educational data mining (EDM) [2] research is student dropout prediction (SDP). SDP is an important educational problem because of the high dropout rate, mainly from e-learning environments. The recent spread of online courses--with enormous numbers of enrolled students, only a fraction of whom complete their studies successfully--has led to increased attention to this problem. As a consequence, there is a growing interest in the adoption of automated systems for predicting student dropout. Automated methodologies have also caught the attention of researchers, particularly in the area of machine learning. Students doing online degree programs have a higher chance of dropping out than those attending conventional classroom environments. According to [3], 40-80% of online students drop out from online classes. Moreover, students may leave courses at any time without notice or further repercussions. Therefore, it is of paramount interest to find more effective methods of addressing the problem of dropout in e-learning environments. The specific objective of SDP in e-learning environments is to model student behavior interacting with e-learning platforms to determine which students are likely to not finish a course, degree, etc. [4]. Predicting student dropout in online learning is a challenge that has been widely studied in the literature [5]. Educational data mining (EDM) techniques have been successfully applied [2] to solve this problem as a binary classification task (zero or one). In this task, there is a set of already labeled training data (students who have already completed the course). Therefore, it is known whether they dropped out (labeled class 1)or successfully completed the course (labeled class 0). The task aims to make the earliest possible prediction of the label or class for new students taking a new course. Traditionally, a wide range of classical machine learning and data mining algorithms have been used to solve this classification problem [6; 7; 8] such as decision trees, Bayesian networks, support vector machines, etc. But in recent years, new, more powerful classification algorithms have appeared, such as deep learning algorithms. They have significantly better predictive capabilities than classical algorithms [9; 10]. In this study, we used a deep learning classifier as an algorithm for predicting student dropout. At the outset, it is important to define a term that we use throughout this paper. The term \"educational entity\" generally refers to any organization or institution that provides educational services, such as a school, college, university, or any other type of learning center. Traditionally, each educational entity builds its own individual predictive model from its own data. These models are commonly referred to as individual or local models. However, this method requires each entity to collect enough data to train its individual model, which takes time and would require resources to create and maintain the prediction model. Furthermore, an entity may be subdivided into smaller entities, for example, a university with its individual faculties, departments, etc., or a group of universities in the same country. In that case, it is crucial to have the necessary data and resources to build and manage the model for each of those subentities and the costs that they entail. There may be subentities with few resources or that do not have the data available to develop their own prediction models. When multiple entities share their data, a more diverse and representative dataset is created, which can result in more robust and more broadly applicable models. However, data sharing also poses significant privacy and security challenges, so any collaborative effort involving data sharing must be conducted with a high level of confidentiality and in compliance with privacy regulations [11; 12]. Conversely, sharing machine learning models between educational entities offers unique benefits and drawbacks.