This paper presents a novel approach to breast cancer classification using federated transfer learning, aiming to enhance patient outcomes and survival rates through early and accurate detection. The study addresses the challenges of limited labeled data and data privacy in healthcare settings by integrating federated learning and transfer learning techniques. The proposed method leverages pre-trained ResNet as a feature extractor, fine-tuning it on breast cancer datasets from multiple medical centers. Domain adversarial training is introduced to mitigate domain shift challenges, ensuring the model learns domain-invariant features. The federated learning framework enables collaborative training without sharing raw data, maintaining data privacy. Extensive experiments on diverse breast cancer datasets from multiple medical centers demonstrate the proposed approach's effectiveness, achieving a classification accuracy of 98.8% with a computational time of 12.22 seconds. The results highlight the potential of this method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment.This paper presents a novel approach to breast cancer classification using federated transfer learning, aiming to enhance patient outcomes and survival rates through early and accurate detection. The study addresses the challenges of limited labeled data and data privacy in healthcare settings by integrating federated learning and transfer learning techniques. The proposed method leverages pre-trained ResNet as a feature extractor, fine-tuning it on breast cancer datasets from multiple medical centers. Domain adversarial training is introduced to mitigate domain shift challenges, ensuring the model learns domain-invariant features. The federated learning framework enables collaborative training without sharing raw data, maintaining data privacy. Extensive experiments on diverse breast cancer datasets from multiple medical centers demonstrate the proposed approach's effectiveness, achieving a classification accuracy of 98.8% with a computational time of 12.22 seconds. The results highlight the potential of this method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment.