## 1 Introduction
By sharing models, entities can benefit from the knowledge of models trained elsewhere. This can expedite the development of effective models, and shared models can serve as strong starting points for modification to specific local contexts. Nevertheless, models may contain implicit information about the original data and may even be susceptible to reverse engineering attacks in certain situations. Therefore, it is critical to ensure that shared models do not compromise data privacy or reveal personal information. Additionally, adapting shared models to local data may require significant resources, and it is important to keep in mind that a model that performs well in one context may not always be immediately applicable in another. There are different approaches where entities can use models already trained elsewhere or share data with other entities to create more reliable and generalizable models for later use. Each approach has its advantages and disadvantages, such as data size, data variability, privacy concerns, and generalization issues. The most common data sharing approach consists of using a centralized learning approach, where data from different sources are gathered together, and then, predictive models are built to find common patterns among all of them. This approach has been widely used by many authors in order to have a one-size-fits-all general model. Some authors who started to consider it as a particular approach for data combination for different sources in education include [13; 14]. However, as those authors stated, the centralized approach has some limitations due to legal and privacy issues, and therefore it is quite often not possible to combine data. Over recent years, several transfer learning studies have been performed in the field of EDM, where models are built in a context (a course, for instance) and transferred to be used in another context. There is a wide variety, as they include very trivial approaches and other, more complex ones. A large group of studies have used deep learning techniques to build models that are subsequently transferred. In [15], the authors suggested that a model trained in one course can be replicated for use in other courses, so that the weights of the target course are initialized with the weights learned in the source course, and from there training is continued towards the target. There have been innovative studies with advanced techniques that use ensembles, which are strategies for combining decisions taken by several machine learning models to improve generalization. This is the proposal in [16], where local training is first performed on the nodes and then two ensemble transfer strategies are used: the first, based on voting (the decision is made based upon the votes from each node); and the second, based on stacking--an approach in which the decisions adopted by the different models feed a new model. In addition, many studies have used a more trivial approach consisting of direct transfer of a model trained on one source for immediate use on the target [17; 18; 19; 20; 21]. When attempting to combine information in that context, there are situations where the data cannot be merged directly. For example, when entities are not authorized to share data due to legal privacy issues [22]. This has led to the emergence of so-called federated learning (FL) methods, which allow multiple entities to build a common machine learning model without sharing data [23]. Most FL methods use the idea of averaging the values provided by the sources for each model parameter, with education being a domain where this idea is also widespread [24; 25]. In [14], average values were calculated of the weights of the neural networks provided by the participating nodes for the prediction of student dropout to demonstrate that this federated approach improved on the results of a centralized approach and avoided the concentration of the data, thus maintaining privacy.