Description: The Joint Learning Framework is a structured approach that enables the implementation of joint learning in federated environments. This framework is based on the idea that multiple entities, such as organizations or devices, can collaborate to train machine learning models without the need to share sensitive or private data. Instead of centralizing data, each participant trains a model locally and only shares the parameters or updates of the model with a central server. This not only preserves data privacy but also allows the model to benefit from a greater diversity of data, which can improve its performance and robustness. The main features of the Joint Learning Framework include decentralization, data privacy, resource efficiency, and the ability to adapt to different environments and requirements. This approach is particularly relevant in a world where data protection is crucial and where organizations seek ways to collaborate without compromising information security. In summary, the Joint Learning Framework represents a significant evolution in how machine learning is approached, enabling effective and secure collaboration among multiple stakeholders.
