Machine learning (ML), particularly deep learning models such as Convolutional Neural Networks (CNNs), has shown great promise in medical imaging, supporting clinicians in diagnosis and treatment planning. However, clinical adoption is often limited by the scarcity of annotated data and the risk of dataset bias, as models trained in a single institution may fail to generalize. In addition, data protection regulations, such as the GDPR, restrict the centralization of medical images across hospitals, limiting the development of robust models. Federated Learning (FL) has emerged as a promising paradigm to address these challenges by enabling multiple institutions to collaboratively train a model without sharing raw data.