Description: The Joint Learning Strategy is an innovative approach that allows multiple entities to collaborate in the process of training artificial intelligence models without the need to share sensitive data. This method is framed within federated learning, where data remains on local devices and only updates of the trained models are sent. The essence of this strategy lies in the preservation of data privacy and security, making it especially relevant in sectors where information is critical, such as healthcare and finance. The main characteristics of the Joint Learning Strategy include the decentralization of the learning process, the reduction of the need to transfer large volumes of data, and the ability to collaboratively improve models. This approach not only optimizes resource use but also fosters innovation by allowing different organizations to contribute their knowledge and expertise. In an increasingly interconnected world, the Joint Learning Strategy presents itself as an effective solution to address the challenges of machine learning while ensuring the protection of sensitive information and compliance with privacy regulations.
