Description: Joint data sharing is a fundamental practice in the field of federated learning, where participants collaborate in creating artificial intelligence models without the need to exchange raw data. Instead of sharing sensitive or private information, participants send updates about the model parameters, allowing each entity to contribute to the learning of the global model. This methodology not only protects data privacy but also reduces the need to transfer large volumes of information, which can be costly and slow. Joint data sharing is based on the premise that models can be effectively trained using only the parameters, enabling organizations to benefit from collective intelligence without compromising information security. This technique is especially relevant in sectors where privacy is critical, such as healthcare, finance, and education. Additionally, it fosters collaboration among different entities, allowing them to leverage the knowledge and experiences of multiple sources, resulting in more robust and accurate models. In summary, joint data sharing is an innovative approach that redefines how collaboration can occur in the development of machine learning models, prioritizing privacy and efficiency.
