Description: Transferability in the context of federated learning refers to the ability of a machine learning model to apply knowledge gained from one task to other different but related tasks. This feature is fundamental as it allows models not only to specialize in a specific dataset but also to generalize their learning to new situations. Transferability is based on the idea that certain patterns and characteristics learned in one domain can be useful in another, thus facilitating adaptation and performance across various applications. In federated learning, where data is kept on local devices and a model is trained in a decentralized manner, transferability becomes even more relevant. Models can benefit from the diversity of data from multiple sources, enhancing their ability to generalize and adapt to different contexts. This property is especially valuable in scenarios where data is scarce or difficult to obtain, as it allows leveraging prior knowledge and experiences from other models or related tasks, thereby optimizing the learning process and improving the efficiency in creating robust and accurate models.
