Abstract: The proposed study will address the increasing mortality rate of breast cancer in women by implementing an automated disease diagnosis system. The system combines federated learning and deep learning to increase efficiency and address the challenge of securely sharing sensitive medical images. This process includes image capture, encryption using an image encryption method, secure data storage using the Federated Learning Flower framework, and disease classification using a deep learning neural network model. The proposed system achieves high performance in terms of precision, recall, precision, and F-measure, as demonstrated through simulation analysis using the Break His database. The results show promise in automated breast cancer diagnosis with improved safety and efficiency.